a research method study

News alert: UC Berkeley has announced its next university librarian

Secondary menu

Log in to your Library account
Hours and Maps
Connect from Off Campus
UC Berkeley Home

Search form

Research methods--quantitative, qualitative, and more: overview.

Quantitative Research
Qualitative Research
Data Science Methods (Machine Learning, AI, Big Data)
Text Mining and Computational Text Analysis
Evidence Synthesis/Systematic Reviews
Get Data, Get Help!

About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley.

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more. This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question.

Suggestions for changes and additions to this guide are welcome!

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods. HERE IS THE ONLINE GUIDE to this one-stop shopping collection, and some helpful links are below:

SAGE Research Methods
Little Green Books (Quantitative Methods)
Little Blue Books (Qualitative Methods)
Dictionaries and Encyclopedias
Case studies of real research projects
Sample datasets for hands-on practice
Streaming video--see methods come to life
Methodspace- -a community for researchers
SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools ! From this link, check out pages for each of the following topics: discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
Wiley Stats Ref for background information on statistics topics
Survey Documentation and Analysis (SDA) . Program for easy web-based analysis of survey data.

Consultants

D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
Sponsored Projects Sponsored projects works with researchers applying for major external grants.
Next: Quantitative Research >>
Last Updated: Apr 25, 2024 11:09 AM
URL: https://guides.lib.berkeley.edu/researchmethods

What Is Research Methodology? A Plain-Language Explanation & Definition (With Examples)

By Derek Jansen (MBA) and Kerryn Warren (PhD) | June 2020 (Last updated April 2023)

If you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

What exactly research methodology means
What qualitative , quantitative and mixed methods are
What sampling strategy is
What data collection methods are
What data analysis methods are
How to choose your research methodology
Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how a researcher systematically designs a study to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

What type of data to collect (e.g., qualitative or quantitative data )
Who to collect it from (i.e., the sampling strategy )
How to collect it (i.e., the data collection method )
How to analyse it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just what methodological choices were made, but also explains why they were made. In other words, the methodology chapter should justify the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions.

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president.

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study. There are many different sampling methods you can choose from, but the two overarching categories are probability sampling and non-probability sampling .

Probability sampling involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable to the entire population.

Non-probability sampling , on the other hand, doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

Interviews (which can be unstructured, semi-structured or structured)
Focus groups and group interviews
Surveys (online or physical surveys)
Observations (watching and recording activities)
Biophysical measurements (e.g., blood pressure, heart rate, etc.)
Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

Qualitative content analysis
Thematic analysis
Discourse analysis
Narrative analysis
Interpretative phenomenological analysis (IPA)
Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some common qualitative analysis methods, along with practical examples.

Moving on to the quantitative side of things, popular data analysis methods in this type of research include:

Descriptive statistics (e.g. means, medians, modes )
Inferential statistics (e.g. correlation, regression, structural equation modelling)

Again, the choice of which data collection method to use depends on your overall research aims and objectives , as well as practicalities and resource constraints. In the video below, we explain some core concepts central to quantitative analysis.

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis).

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components.

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

You Might Also Like:

199 Comments

Thank you for this simple yet comprehensive and easy to digest presentation. God Bless!

You’re most welcome, Leo. Best of luck with your research!

I found it very useful. many thanks

This is really directional. A make-easy research knowledge.

Thank you for this, I think will help my research proposal

Thanks for good interpretation,well understood.

Good morning sorry I want to the search topic

Thank u more

Thank you, your explanation is simple and very helpful.

Very educative a.nd exciting platform. A bigger thank you and I’ll like to always be with you

That’s the best analysis

So simple yet so insightful. Thank you.

This really easy to read as it is self-explanatory. Very much appreciated…

Thanks for this. It’s so helpful and explicit. For those elements highlighted in orange, they were good sources of referrals for concepts I didn’t understand. A million thanks for this.

Good morning, I have been reading your research lessons through out a period of times. They are important, impressive and clear. Want to subscribe and be and be active with you.

Thankyou So much Sir Derek…

Good morning thanks so much for the on line lectures am a student of university of Makeni.select a research topic and deliberate on it so that we’ll continue to understand more.sorry that’s a suggestion.

Beautiful presentation. I love it.

please provide a research mehodology example for zoology

It’s very educative and well explained

Thanks for the concise and informative data.

This is really good for students to be safe and well understand that research is all about

Thank you so much Derek sir🖤🙏🤗

Very simple and reliable

This is really helpful. Thanks alot. God bless you.

very useful, Thank you very much..

thanks a lot its really useful

in a nutshell..thank you!

Thanks for updating my understanding on this aspect of my Thesis writing.

thank you so much my through this video am competently going to do a good job my thesis

Thanks a lot. Very simple to understand. I appreciate 🙏

Very simple but yet insightful Thank you

This has been an eye opening experience. Thank you grad coach team.

Very useful message for research scholars

Really very helpful thank you

yes you are right and i’m left

Research methodology with a simplest way i have never seen before this article.

wow thank u so much

Good morning thanks so much for the on line lectures am a student of university of Makeni.select a research topic and deliberate on is so that we will continue to understand more.sorry that’s a suggestion.

Very precise and informative.

Thanks for simplifying these terms for us, really appreciate it.

Thanks this has really helped me. It is very easy to understand.

I found the notes and the presentation assisting and opening my understanding on research methodology

Good presentation

Im so glad you clarified my misconceptions. Im now ready to fry my onions. Thank you so much. God bless

Thank you a lot.

thanks for the easy way of learning and desirable presentation.

Thanks a lot. I am inspired

Well written

I am writing a APA Format paper . I using questionnaire with 120 STDs teacher for my participant. Can you write me mthology for this research. Send it through email sent. Just need a sample as an example please. My topic is ” impacts of overcrowding on students learning

Thanks for your comment.

We can’t write your methodology for you. If you’re looking for samples, you should be able to find some sample methodologies on Google. Alternatively, you can download some previous dissertations from a dissertation directory and have a look at the methodology chapters therein.

All the best with your research.

Thank you so much for this!! God Bless

Thank you. Explicit explanation

Thank you, Derek and Kerryn, for making this simple to understand. I’m currently at the inception stage of my research.

Thnks a lot , this was very usefull on my assignment

excellent explanation

I’m currently working on my master’s thesis, thanks for this! I’m certain that I will use Qualitative methodology.

Thanks a lot for this concise piece, it was quite relieving and helpful. God bless you BIG…

I am currently doing my dissertation proposal and I am sure that I will do quantitative research. Thank you very much it was extremely helpful.

Very interesting and informative yet I would like to know about examples of Research Questions as well, if possible.

I’m about to submit a research presentation, I have come to understand from your simplification on understanding research methodology. My research will be mixed methodology, qualitative as well as quantitative. So aim and objective of mixed method would be both exploratory and confirmatory. Thanks you very much for your guidance.

OMG thanks for that, you’re a life saver. You covered all the points I needed. Thank you so much ❤️ ❤️ ❤️

Thank you immensely for this simple, easy to comprehend explanation of data collection methods. I have been stuck here for months 😩. Glad I found your piece. Super insightful.

I’m going to write synopsis which will be quantitative research method and I don’t know how to frame my topic, can I kindly get some ideas..

Thanks for this, I was really struggling.

This was really informative I was struggling but this helped me.

Thanks a lot for this information, simple and straightforward. I’m a last year student from the University of South Africa UNISA South Africa.

its very much informative and understandable. I have enlightened.

An interesting nice exploration of a topic.

Thank you. Accurate and simple🥰

This article was really helpful, it helped me understanding the basic concepts of the topic Research Methodology. The examples were very clear, and easy to understand. I would like to visit this website again. Thank you so much for such a great explanation of the subject.

Thanks dude

Thank you Doctor Derek for this wonderful piece, please help to provide your details for reference purpose. God bless.

Many compliments to you

Great work , thank you very much for the simple explanation

Thank you. I had to give a presentation on this topic. I have looked everywhere on the internet but this is the best and simple explanation.

thank you, its very informative.

Well explained. Now I know my research methodology will be qualitative and exploratory. Thank you so much, keep up the good work

Well explained, thank you very much.

This is good explanation, I have understood the different methods of research. Thanks a lot.

Great work…very well explanation

Thanks Derek. Kerryn was just fantastic!

Great to hear that, Hyacinth. Best of luck with your research!

Its a good templates very attractive and important to PhD students and lectuter

Thanks for the feedback, Matobela. Good luck with your research methodology.

Thank you. This is really helpful.

You’re very welcome, Elie. Good luck with your research methodology.

Well explained thanks

This is a very helpful site especially for young researchers at college. It provides sufficient information to guide students and equip them with the necessary foundation to ask any other questions aimed at deepening their understanding.

Thanks for the kind words, Edward. Good luck with your research!

Thank you. I have learned a lot.

Great to hear that, Ngwisa. Good luck with your research methodology!

Thank you for keeping your presentation simples and short and covering key information for research methodology. My key takeaway: Start with defining your research objective the other will depend on the aims of your research question.

My name is Zanele I would like to be assisted with my research , and the topic is shortage of nursing staff globally want are the causes , effects on health, patients and community and also globally

Thanks for making it simple and clear. It greatly helped in understanding research methodology. Regards.

This is well simplified and straight to the point

Thank you Dr

I was given an assignment to research 2 publications and describe their research methodology? I don’t know how to start this task can someone help me?

Sure. You’re welcome to book an initial consultation with one of our Research Coaches to discuss how we can assist – https://gradcoach.com/book/new/ .

Thanks a lot I am relieved of a heavy burden.keep up with the good work

I’m very much grateful Dr Derek. I’m planning to pursue one of the careers that really needs one to be very much eager to know. There’s a lot of research to do and everything, but since I’ve gotten this information I will use it to the best of my potential.

Thank you so much, words are not enough to explain how helpful this session has been for me!

Thanks this has thought me alot.

Very concise and helpful. Thanks a lot

Thank Derek. This is very helpful. Your step by step explanation has made it easier for me to understand different concepts. Now i can get on with my research.

I wish i had come across this sooner. So simple but yet insightful

really nice explanation thank you so much

I’m so grateful finding this site, it’s really helpful…….every term well explained and provide accurate understanding especially to student going into an in-depth research for the very first time, even though my lecturer already explained this topic to the class, I think I got the clear and efficient explanation here, much thanks to the author.

It is very helpful material

I would like to be assisted with my research topic : Literature Review and research methodologies. My topic is : what is the relationship between unemployment and economic growth?

Its really nice and good for us.

THANKS SO MUCH FOR EXPLANATION, ITS VERY CLEAR TO ME WHAT I WILL BE DOING FROM NOW .GREAT READS.

Short but sweet.Thank you

Informative article. Thanks for your detailed information.

I’m currently working on my Ph.D. thesis. Thanks a lot, Derek and Kerryn, Well-organized sequences, facilitate the readers’ following.

great article for someone who does not have any background can even understand

I am a bit confused about research design and methodology. Are they the same? If not, what are the differences and how are they related?

Thanks in advance.

concise and informative.

Thank you very much

How can we site this article is Harvard style?

Very well written piece that afforded better understanding of the concept. Thank you!

Am a new researcher trying to learn how best to write a research proposal. I find your article spot on and want to download the free template but finding difficulties. Can u kindly send it to my email, the free download entitled, “Free Download: Research Proposal Template (with Examples)”.

Thank too much

Thank you very much for your comprehensive explanation about research methodology so I like to thank you again for giving us such great things.

Good very well explained.Thanks for sharing it.

Thank u sir, it is really a good guideline.

so helpful thank you very much.

Thanks for the video it was very explanatory and detailed, easy to comprehend and follow up. please, keep it up the good work

It was very helpful, a well-written document with precise information.

how do i reference this?

MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

Your explanation is easily understood. Thank you

Very help article. Now I can go my methodology chapter in my thesis with ease

I feel guided ,Thank you

This simplification is very helpful. It is simple but very educative, thanks ever so much

The write up is informative and educative. It is an academic intellectual representation that every good researcher can find useful. Thanks

Wow, this is wonderful long live.

Nice initiative

thank you the video was helpful to me.

Thank you very much for your simple and clear explanations I’m really satisfied by the way you did it By now, I think I can realize a very good article by following your fastidious indications May God bless you

Thanks very much, it was very concise and informational for a beginner like me to gain an insight into what i am about to undertake. I really appreciate.

very informative sir, it is amazing to understand the meaning of question hidden behind that, and simple language is used other than legislature to understand easily. stay happy.

This one is really amazing. All content in your youtube channel is a very helpful guide for doing research. Thanks, GradCoach.

research methodologies

Please send me more information concerning dissertation research.

Nice piece of knowledge shared….. #Thump_UP

This is amazing, it has said it all. Thanks to Gradcoach

This is wonderful,very elaborate and clear.I hope to reach out for your assistance in my research very soon.

This is the answer I am searching about…

realy thanks a lot

Thank you very much for this awesome, to the point and inclusive article.

Thank you very much I need validity and reliability explanation I have exams

Thank you for a well explained piece. This will help me going forward.

Very simple and well detailed Many thanks

This is so very simple yet so very effective and comprehensive. An Excellent piece of work.

I wish I saw this earlier on! Great insights for a beginner(researcher) like me. Thanks a mil!

Thank you very much, for such a simplified, clear and practical step by step both for academic students and general research work. Holistic, effective to use and easy to read step by step. One can easily apply the steps in practical terms and produce a quality document/up-to standard

Thanks for simplifying these terms for us, really appreciated.

Thanks for a great work. well understood .

This was very helpful. It was simple but profound and very easy to understand. Thank you so much!

Great and amazing research guidelines. Best site for learning research

hello sir/ma’am, i didn’t find yet that what type of research methodology i am using. because i am writing my report on CSR and collect all my data from websites and articles so which type of methodology i should write in dissertation report. please help me. i am from India.

how does this really work?

perfect content, thanks a lot

As a researcher, I commend you for the detailed and simplified information on the topic in question. I would like to remain in touch for the sharing of research ideas on other topics. Thank you

Impressive. Thank you, Grad Coach 😍

Thank you Grad Coach for this piece of information. I have at least learned about the different types of research methodologies.

Very useful content with easy way

Thank you very much for the presentation. I am an MPH student with the Adventist University of Africa. I have successfully completed my theory and starting on my research this July. My topic is “Factors associated with Dental Caries in (one District) in Botswana. I need help on how to go about this quantitative research

I am so grateful to run across something that was sooo helpful. I have been on my doctorate journey for quite some time. Your breakdown on methodology helped me to refresh my intent. Thank you.

thanks so much for this good lecture. student from university of science and technology, Wudil. Kano Nigeria.

It’s profound easy to understand I appreciate

Thanks a lot for sharing superb information in a detailed but concise manner. It was really helpful and helped a lot in getting into my own research methodology.

Comment * thanks very much

This was sooo helpful for me thank you so much i didn’t even know what i had to write thank you!

You’re most welcome 🙂

Simple and good. Very much helpful. Thank you so much.

This is very good work. I have benefited.

Thank you so much for sharing

This is powerful thank you so much guys

I am nkasa lizwi doing my research proposal on honors with the university of Walter Sisulu Komani I m on part 3 now can you assist me.my topic is: transitional challenges faced by educators in intermediate phase in the Alfred Nzo District.

Appreciate the presentation. Very useful step-by-step guidelines to follow.

I appreciate sir

wow! This is super insightful for me. Thank you!

Indeed this material is very helpful! Kudos writers/authors.

I want to say thank you very much, I got a lot of info and knowledge. Be blessed.

I want present a seminar paper on Optimisation of Deep learning-based models on vulnerability detection in digital transactions.

Need assistance

Dear Sir, I want to be assisted on my research on Sanitation and Water management in emergencies areas.

I am deeply grateful for the knowledge gained. I will be getting in touch shortly as I want to be assisted in my ongoing research.

The information shared is informative, crisp and clear. Kudos Team! And thanks a lot!

hello i want to study

Hello!! Grad coach teams. I am extremely happy in your tutorial or consultation. i am really benefited all material and briefing. Thank you very much for your generous helps. Please keep it up. If you add in your briefing, references for further reading, it will be very nice.

All I have to say is, thank u gyz.

Good, l thanks

thank you, it is very useful

Trackbacks/Pingbacks

What Is A Literature Review (In A Dissertation Or Thesis) - Grad Coach - […] the literature review is to inform the choice of methodology for your own research. As we’ve discussed on the Grad Coach blog,…
Free Download: Research Proposal Template (With Examples) - Grad Coach - […] Research design (methodology) […]
Dissertation vs Thesis: What's the difference? - Grad Coach - […] and thesis writing on a daily basis – everything from how to find a good research topic to which…

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Print Friendly

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

Knowledge Base
Methodology

Research Methods | Definition, Types, Examples

Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs quantitative : Will your data take the form of words or numbers?
Primary vs secondary : Will you collect original data yourself, or will you use data that have already been collected by someone else?
Descriptive vs experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyse the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analysing data, examples of data analysis methods, frequently asked questions about methodology.

Data are the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.

Primary vs secondary data

Primary data are any original information that you collect for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary data are information that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data. But if you want to synthesise existing knowledge, analyse historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

Prevent plagiarism, run a free check.

Your data analysis methods will depend on the type of data you collect and how you prepare them for analysis.

Data can often be analysed both quantitatively and qualitatively. For example, survey responses could be analysed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that were collected:

From open-ended survey and interview questions, literature reviews, case studies, and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions.

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that were collected either:

During an experiment.
Using probability sampling methods .

Because the data are collected and analysed in a statistically valid way, the results of quantitative analysis can be easily standardised and shared among researchers.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

Is this article helpful?

Research Methods Guide: Research Design & Method

Introduction
Survey Research
Interview Research
Data Analysis
Resources & Consultation

Tutorial Videos: Research Design & Method

Research Methods (sociology-focused)

Qualitative vs. Quantitative Methods (intro)

Qualitative vs. Quantitative Methods (advanced)

FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

Research design is a plan to answer your research question. A research method is a strategy used to implement that plan. Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively.

Which research method should I choose ?

It depends on your research goal. It depends on what subjects (and who) you want to study. Let's say you are interested in studying what makes people happy, or why some students are more conscious about recycling on campus. To answer these questions, you need to make a decision about how to collect your data. Most frequently used methods include:

Observation / Participant Observation
Focus Groups
Experiments
Secondary Data Analysis / Archival Study
Mixed Methods (combination of some of the above)

One particular method could be better suited to your research goal than others, because the data you collect from different methods will be different in quality and quantity. For instance, surveys are usually designed to produce relatively short answers, rather than the extensive responses expected in qualitative interviews.

What other factors should I consider when choosing one method over another?

Time for data collection and analysis is something you want to consider. An observation or interview method, so-called qualitative approach, helps you collect richer information, but it takes time. Using a survey helps you collect more data quickly, yet it may lack details. So, you will need to consider the time you have for research and the balance between strengths and weaknesses associated with each method (e.g., qualitative vs. quantitative).

<< Previous: Introduction
Next: Survey Research >>
Last Updated: Aug 21, 2023 10:42 AM

Research Methodologies

What are research designs?
What are research methodologies?

What are research methods?

Quantitative research methods, qualitative research methods, mixed method approach, selecting the best research method.

Additional Sources

Research methods are different from research methodologies because they are the ways in which you will collect the data for your research project. The best method for your project largely depends on your topic, the type of data you will need, and the people or items from which you will be collecting data. The following boxes below contain a list of quantitative, qualitative, and mixed research methods.

Closed-ended questionnaires/survey: These types of questionnaires or surveys are like "multiple choice" tests, where participants must select from a list of premade answers. According to the content of the question, they must select the one that they agree with the most. This approach is the simplest form of quantitative research because the data is easy to combine and quantify.
Structured interviews: These are a common research method in market research because the data can be quantified. They are strictly designed for little "wiggle room" in the interview process so that the data will not be skewed. You can conduct structured interviews in-person, online, or over the phone (Dawson, 2019).

Constructing Questionnaires

When constructing your questions for a survey or questionnaire, there are things you can do to ensure that your questions are accurate and easy to understand (Dawson, 2019):

Keep the questions brief and simple.
Eliminate any potential bias from your questions. Make sure that they do not word things in a way that favor one perspective over another.
If your topic is very sensitive, you may want to ask indirect questions rather than direct ones. This prevents participants from being intimidated and becoming unwilling to share their true responses.
If you are using a closed-ended question, try to offer every possible answer that a participant could give to that question.
Do not ask questions that assume something of the participant. The question "How often do you exercise?" assumes that the participant exercises (when they may not), so you would want to include a question that asks if they exercise at all before asking them how often.
Try and keep the questionnaire as short as possible. The longer a questionnaire takes, the more likely the participant will not complete it or get too tired to put truthful answers.
Promise confidentiality to your participants at the beginning of the questionnaire.

Quantitative Research Measures

When you are considering a quantitative approach to your research, you need to identify why types of measures you will use in your study. This will determine what type of numbers you will be using to collect your data. There are four levels of measurement:

Nominal: These are numbers where the order of the numbers do not matter. They aim to identify separate information. One example is collecting zip codes from research participants. The order of the numbers does not matter, but the series of numbers in each zip code indicate different information (Adamson and Prion, 2013).
Ordinal: Also known as rankings because the order of these numbers matter. This is when items are given a specific rank according to specific criteria. A common example of ordinal measurements include ranking-based questionnaires, where participants are asked to rank items from least favorite to most favorite. Another common example is a pain scale, where a patient is asked to rank their pain on a scale from 1 to 10 (Adamson and Prion, 2013).
Interval: This is when the data are ordered and the distance between the numbers matters to the researcher (Adamson and Prion, 2013). The distance between each number is the same. An example of interval data is test grades.
Ratio: This is when the data are ordered and have a consistent distance between numbers, but has a "zero point." This means that there could be a measurement of zero of whatever you are measuring in your study (Adamson and Prion, 2013). An example of ratio data is measuring the height of something because the "zero point" remains constant in all measurements. The height of something could also be zero.

Focus Groups

This is when a select group of people gather to talk about a particular topic. They can also be called discussion groups or group interviews (Dawson, 2019). They are usually lead by a moderator to help guide the discussion and ask certain questions. It is critical that a moderator allows everyone in the group to get a chance to speak so that no one dominates the discussion. The data that are gathered from focus groups tend to be thoughts, opinions, and perspectives about an issue.

Advantages of Focus Groups

Only requires one meeting to get different types of responses.
Less researcher bias due to participants being able to speak openly.
Helps participants overcome insecurities or fears about a topic.
The researcher can also consider the impact of participant interaction.

Disadvantages of Focus Groups

Participants may feel uncomfortable to speak in front of an audience, especially if the topic is sensitive or controversial.
Since participation is voluntary, not every participant may contribute equally to the discussion.
Participants may impact what others say or think.
A researcher may feel intimidated by running a focus group on their own.
A researcher may need extra funds/resources to provide a safe space to host the focus group.
Because the data is collective, it may be difficult to determine a participant's individual thoughts about the research topic.

Observation

There are two ways to conduct research observations:

Direct Observation: The researcher observes a participant in an environment. The researcher often takes notes or uses technology to gather data, such as a voice recorder or video camera. The researcher does not interact or interfere with the participants. This approach is often used in psychology and health studies (Dawson, 2019).
Participant Observation: The researcher interacts directly with the participants to get a better understanding of the research topic. This is a common research method when trying to understand another culture or community. It is important to decide if you will conduct a covert (participants do not know they are part of the research) or overt (participants know the researcher is observing them) observation because it can be unethical in some situations (Dawson, 2019).

Open-Ended Questionnaires

These types of questionnaires are the opposite of "multiple choice" questionnaires because the answer boxes are left open for the participant to complete. This means that participants can write short or extended answers to the questions. Upon gathering the responses, researchers will often "quantify" the data by organizing the responses into different categories. This can be time consuming because the researcher needs to read all responses carefully.

Semi-structured Interviews

This is the most common type of interview where researchers aim to get specific information so they can compare it to other interview data. This requires asking the same questions for each interview, but keeping their responses flexible. This means including follow-up questions if a subject answers a certain way. Interview schedules are commonly used to aid the interviewers, which list topics or questions that will be discussed at each interview (Dawson, 2019).

Theoretical Analysis

Often used for nonhuman research, theoretical analysis is a qualitative approach where the researcher applies a theoretical framework to analyze something about their topic. A theoretical framework gives the researcher a specific "lens" to view the topic and think about it critically. it also serves as context to guide the entire study. This is a popular research method for analyzing works of literature, films, and other forms of media. You can implement more than one theoretical framework with this method, as many theories complement one another.

Common theoretical frameworks for qualitative research are (Grant and Osanloo, 2014):

Behavioral theory
Change theory
Cognitive theory
Content analysis
Cross-sectional analysis
Developmental theory
Feminist theory
Gender theory
Marxist theory
Queer theory
Systems theory
Transformational theory

Unstructured Interviews

These are in-depth interviews where the researcher tries to understand an interviewee's perspective on a situation or issue. They are sometimes called life history interviews. It is important not to bombard the interviewee with too many questions so they can freely disclose their thoughts (Dawson, 2019).

Open-ended and closed-ended questionnaires: This approach means implementing elements of both questionnaire types into your data collection. Participants may answer some questions with premade answers and write their own answers to other questions. The advantage to this method is that you benefit from both types of data collection to get a broader understanding of you participants. However, you must think carefully about how you will analyze this data to arrive at a conclusion.

Other mixed method approaches that incorporate quantitative and qualitative research methods depend heavily on the research topic. It is strongly recommended that you collaborate with your academic advisor before finalizing a mixed method approach.

How do you determine which research method would be best for your proposal? This heavily depends on your research objective. According to Dawson (2019), there are several questions to ask yourself when determining the best research method for your project:

Are you good with numbers and mathematics?
Would you be interested in conducting interviews with human subjects?
Would you enjoy creating a questionnaire for participants to complete?
Do you prefer written communication or face-to-face interaction?
What skills or experiences do you have that might help you with your research? Do you have any experiences from past research projects that can help with this one?
How much time do you have to complete the research? Some methods take longer to collect data than others.
What is your budget? Do you have adequate funding to conduct the research in the method you want?
How much data do you need? Some research topics need only a small amount of data while others may need significantly larger amounts.
What is the purpose of your research? This can provide a good indicator as to what research method will be most appropriate.
<< Previous: What are research methodologies?
Next: Additional Sources >>
Last Updated: Aug 2, 2022 2:36 PM
URL: https://library.tiffin.edu/researchmethodologies

Research Methods: What are research methods?

What are research methods?
Searching specific databases

What are research methods

Research methods are the strategies, processes or techniques utilized in the collection of data or evidence for analysis in order to uncover new information or create better understanding of a topic.

There are different types of research methods which use different tools for data collection.

Types of research

Qualitative Research
Quantitative Research
Mixed Methods Research

Qualitative Research gathers data about lived experiences, emotions or behaviours, and the meanings individuals attach to them. It assists in enabling researchers to gain a better understanding of complex concepts, social interactions or cultural phenomena. This type of research is useful in the exploration of how or why things have occurred, interpreting events and describing actions.

Quantitative Research gathers numerical data which can be ranked, measured or categorised through statistical analysis. It assists with uncovering patterns or relationships, and for making generalisations. This type of research is useful for finding out how many, how much, how often, or to what extent.

Mixed Methods Research integrates both Q ualitative and Quantitative Research . It provides a holistic approach combining and analysing the statistical data with deeper contextualised insights. Using Mixed Methods also enables Triangulation, or verification, of the data from two or more sources.

Finding Mixed Methods research in the Databases

“mixed model*” OR “mixed design*” OR “multiple method*” OR multimethod* OR triangulat*

Data collection tools

Sage research methods.

SAGE research methods online This link opens in a new window Research methods tool to help researchers gather full-text resources, design research projects, understand a particular method and write up their research. Includes access to collections of video, business cases and eBooks,

Help and Information

Next: Finding qualitative research >>
Last Updated: Apr 18, 2024 11:16 AM
URL: https://libguides.newcastle.edu.au/researchmethods

Research Methods In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other.

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null. The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period.

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them.

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables.

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization.
Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way.

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time.

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures.

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

types of correlation. Scatter plot. Positive negative and no correlation

If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

Types of correlation. Strong, weak, and perfect positive correlation, strong, weak, and perfect negative correlation, no correlation. Graphs or charts ...

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved.

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way.

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view.

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
Natural : Here, spontaneous behavior is recorded in a natural setting.
Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.
Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

Test-retest reliability : assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
Primary data is first-hand data collected for the purpose of the investigation.
Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

Open access
Published: 06 May 2024

Breaking bad news: A mix methods study reporting the need for improving communication skills among doctors in Pakistan

Muhammad Ahmed Abdullah 1 ,
Babar Tasneem Shaikh 1 ,
Kashif Rehman Khan 2 &
Muhammad Asif Yasin 3

BMC Health Services Research volume 24 , Article number: 588 ( 2024 ) Cite this article

17 Accesses

Metrics details

Effective skills and training for physicians are essential for communicating difficult or distressing information, also known as breaking bad news (BBN). This study aimed to assess both the capacity and the practices of clinicians in Pakistan regarding BBN.

A cross-sectional study was conducted involving 151 clinicians. Quantitative component used a structured questionnaire, while qualitative data were obtained through in-depth interviews with 13 medical educationists. The responses were analyzed using descriptive statistics and thematic analysis.

While most clinicians acknowledged their responsibility of delivering difficult news, only a small percentage had received formal training in BBN. Areas for improvement include time and interruption management, rapport building, and understanding the patients’ point of view. Prognosis and treatment options were not consistently discussed. Limited importance is given to BBN in medical education.

Training in BBN will lead to improved patient and attendants’ satisfaction, and empathetic support during difficult times.

Peer Review reports

Introduction

The duties of physicians extend beyond providing an effective treatment to patients; they also encompass the development of strong communication skills and the establishment of trust with their patients [ 1 ]. This emphasis on communication is crucial as it enables patients to cope with the seriousness and severity of their illnesses, to make informed decisions regarding treatment options, and to manage potential side effects [ 2 ]. In recent years, there has been a shift in medical practice from a doctor-centered approach to a patient-centered one, where patients play a significant role in the decision-making process, ultimately leading to increased patient satisfaction [ 3 ]. However, physicians may find themselves burdened when faced with the task of breaking bad news, fearing the potential reactions of their patients [ 4 , 5 ]. Neglecting to address this challenge can have negative consequences in terms of patient-centered healthcare, as physicians’ reluctance to disclose a bad news may compromise mental and physical well-being of the patients, and at times of the family members too [ 6 ]. On the other hand, physicians are being uncomfortable with their own emotions and do not have enough coping skills to manage their emotions in the moment [ 7 ].

Research studies have documented the lack of training and protocols among doctors for breaking bad news. For instance, a research from Brazil revealed that none of the clinicians at a university hospital were aware of any specific protocol or guidelines for this purpose [ 5 ]. Similarly, in Canada and South Korea, physician training in breaking bad news is reported to be insufficient, and in many underdeveloped countries, it is virtually non-existent despite curricular reforms [ 8 ]. In Northern Portugal, a significant number of family physicians expressed apprehension about breaking bad news and deemed training in this area necessary [ 9 ]. In Iran, inadequate training was identified as the main reason behind physicians’ difficulty and fear in delivering bad news to patients, emphasizing the need for formal training in this domain [ 1 ]. In India, one research documented diverse opinions among oncologists regarding breaking bad news and sharing information with patients, accenting the necessity for physician training in this aspect [ 10 ]. Additionally, a study conducted in Pakistan identified a common reason for increasing violence against healthcare providers as the failure to communicate bad news in a timely and appropriate manner, highlighting the need for better preparation and communication skills during this process [ 4 ]. Several protocols and guidelines have been developed for breaking bad news, with the SPIKES protocol being one of the most widely used due to its comprehensive coverage of essential aspects, particularly the emotional aspect of the process [ 11 ]. This Six-Step Protocol for Delivering Bad News is SPIKES: S for setting up the meeting, P is assessing the patient’s perception, I for achieving the patient’s invitation, K is providing knowledge and information to the patient, E is addressing the patient’s emotions with empathic responses and S for strategy and summary.

Despite the recommendations of the Pakistan Medical and Dental Council to incorporate communication skills into formal medical curricula, and the ongoing discussions regarding medical curricular reforms in Pakistan over the past two decades, little progress has been made in this regard. This lack of action is evident from a recent study conducted in Peshawar, Pakistan [ 12 ]. Thus, the aim of our study was to assess the training as well as the practices of clinicians in Pakistan regarding BBN and provide recommendations for improvement.

Study design

This mixed methods study utilized a cross-sectional design to assess the training and practices of doctors in BBN. The study was conducted at five tertiary care hospitals located in the twin cities of Islamabad and Rawalpindi, namely, Akbar Niazi Teaching Hospital, Benazir Bhutto Hospital, Holy Family Hospital, NESCOM Hospital, and Combined Military Hospital. The data collection period was eight weeks in the first quarter of 2023 to ensure an adequate sample size and data representation. The study participants selected through a simple random sampling included medical personnel directly involved in healthcare delivery within the selected hospitals with a minimum of six months of clinical experience. Medical students and Basic Health Sciences faculty were excluded from the study sample.

Data collection

To collect the necessary data, a 25-item self-administered questionnaire was developed. The questionnaire encompassed two main sections. The first section focused on recording participants’ demographic information, including age, gender, designation, specialty, and years of experience. This section aimed to establish a comprehensive profile of the participating doctors, providing a contextual background for the subsequent analysis of their responses. The second section of the questionnaire delved into the participants’ knowledge and practices related to breaking bad news, drawing from the established SPIKES protocol [ 11 ]. This section comprised a series of questions designed to assess the doctors’ familiarity with the protocol, their adherence to its guidelines, and their overall comfort level in delivering challenging news to patients and their families. The SPIKES protocol, which stands for Setting, Perception, Invitation, Knowledge, Emotions, and Strategy, is a widely recognized framework for effective communication during difficult conversations. Before administering the questionnaire, a pilot study was conducted with ten doctors working in general practice clinics, in Rawalpindi/Islamabad, to ensure its clarity, comprehensibility, and relevance to the research objectives. Feedback from the pilot study participants was incorporated into the final version of the questionnaire to enhance its validity and reliability.

Sample size calculation

The sample size for this study was determined based on a 95% confidence level, considering a hypothesized population proportion of 11% with a 5% margin of error. The anticipated frequency of this outcome factor was derived from a previous study [ 13 ]. The population size was estimated to be 200,000. Using the formula for sample size calculation for frequency in a population (n = [DEFF * N * p * (1-p)] / [(d^2 / Z^2) * (N-1) + p * (1-p)]), where DEFF represents the design effect, N is the population size, p is the hypothesized proportion, d is the margin of error, and Z is the critical value corresponding to the desired confidence level, the required sample size was determined to be approximately 151 participants.

Data analysis and synthesis

After data collection, the collected data were subjected to comprehensive analysis using SPSS version 22.0. Descriptive statistics, such as frequencies and percentages were computed to summarize the data and gain insights into the training and practices of doctors in breaking bad news.

The qualitative part of the study aimed to gain insights into the practices and challenges associated with breaking bad news in a healthcare setting. The qualitative data were gathered through in-depth interviews with 13 medical educationists from Pakistan. Each interview lasted between 30 and 45 min and took place in the office spaces of the participants to ensure privacy and confidentiality. The participants were individuals who had been involved in teaching medicine for at least 5 years, including 6 clinicians, 4 individuals from medical education, and 3 from basic sciences departments. The interviews were conducted by the principal investigator along with a medical student who accompanied as a note-taker. Rigorous note-taking was done during the interviews to capture detailed information, and where possible, the interviews were audio recorded and later transcribed for analysis. The Braun and Clarke’s thematic analysis method was used as an iterative process which consisted of six steps: (1) becoming familiar with the data, (2) generating codes, (3) generating themes, (4) reviewing themes, (5) defining and naming themes, and (6) locating exemplars [ 14 ]. The analysis was conducted by carefully reading and familiarizing with the interview transcripts. Codes were generated to label and categorize meaningful segments of data, which were refined and grouped into broader themes. The research team engaged in discussions to validate the emerging themes and ensure the reliability of the analysis.

Demographic data of the participants showed that out of the total 151 respondents males were greater in number than females (62.3%), mean age was 30.7(± 8.6 SD) years and the proportion of house officers was the highest, as shown in Table 1 . Response rate of the employees of private hospitals was higher than that of the public sector and there were graduates from several medical institutions all over Pakistan.

Table 2 illustrates the responses to various questions related to BBN. Out of the total respondents, 74% reported that BBN was included in their daily duties, indicating that a significant majority of doctors in Pakistan are involved in delivering difficult news to their patients. However, only 9% of the participants reported receiving training specifically focused on BBN, while the remaining 91% had not received such training.

When considering the tenure of the BBN training, a small percentage of doctors (2%) reported receiving training during their MBBS education, followed by 3% during their house job, and 3% during postgraduate training. Surprisingly, the majority of respondents (92%) relied on personal experience rather than formal training to navigate the challenges of BBN. Regarding the availability of formal guidelines for BBN, only 10% of the participants reported having access to such guidelines, while the majority (90%) did not have formal guidelines to follow.

Maintaining privacy during the process of BBN was reported by 14% of the participants, indicating that privacy considerations may not be adequately addressed in some healthcare settings. Similarly, patient attendants’ involvement during the BBN was reported by 78% of the respondents, suggesting that involving family members or caregivers in the process is common.

When it comes to communication techniques during BBN, 64% of doctors reported sitting while delivering the news, while 36% did not. Time and interruption management, rapport building, patient perception exploration, and adequate patient speaking time were areas where improvements were needed, as reported by the participants.

Furthermore, while 52% of the respondents reported avoiding excessive bluntness and handling emotions appropriately, a considerable portion (48%) did not prioritize these aspects. Identification of emotional state, empathic response, and providing time for personal expression were areas where improvements were necessary, as reported by the participants. Moreover, the participants acknowledged the importance of avoiding jargon and technical terms (44%) and breaking the information into small chunks (45%) to enhance patient understanding. However, further efforts were needed to ensure that hopelessness was avoided during the conversation (50%).

Regarding prognosis and treatment options, 20% of the doctors reported discussing these aspects during BBN conversations, indicating that there is room for improvement in ensuring comprehensive information delivery and empathetic counseling.

In summary, the results highlight several areas where training and guidelines for BBN in Pakistan can be improved. The majority of doctors rely on personal experience rather than formal training, indicating a need for structured educational programs and guidelines in this critical area of healthcare communication. Privacy considerations, effective communication techniques, and emotional support for patients were identified as areas that require further attention and development. The findings emphasize the importance of enhancing training and providing formal guidelines to equip doctors with the necessary skills and strategies for delivering difficult news effectively and compassionately.

The qualitative component of the study involved in-depth interviews with 13 medical educationists from Pakistan. These interviews aimed to explore the level and standard of training on BBN in the curriculum and training of doctors in Pakistan. The interviews revealed several key themes that shed light on the current state of training and education in this area.

Theme 1: ambiguity in subject domains and integration of communication skills

The medical educationists expressed concerns regarding the lack of clarity in subject domains and the integration of communication skills into the medical curriculum. They suggested that communication skills, including BBN, should be incorporated into the community medicine curriculum. Furthermore, they proposed the introduction of family medicine as a dedicated subject at the undergraduate level, which would provide comprehensive training in communication skills and prepare doctors to handle sensitive conversations effectively.

One interviewee highlighted, “There is a lack of clarity when it comes to subject domains and the inclusion of communication skills in our medical curriculum. We believe that communication skills, including breaking bad news, should be integrated into the community medicine curriculum. Additionally, introducing family medicine as a dedicated subject at the undergraduate level would ensure that doctors receive extensive training in effective communication, addressing the emotional needs of patients and their families.” [P6].

This theme emphasizes the need for clear subject domains and the integration of communication skills including BBN within medical education. The proposal to introduce family medicine as an undergraduate subject reflects a holistic approach to training future doctors in effectively delivering difficult news and addressing the diverse needs of patients and their families.

Theme 2: limited importance of breaking bad news in medical education

The medical educationists expressed that at present BBN does not hold a significant place in the teaching and training of doctors in Pakistan. The focus is primarily on technical clinical knowledge and skill development, often neglecting important soft skills such as communication skills, research skills, and logistics. This lack of emphasis on communication training implies that doctors may not be adequately prepared to handle the complexities of BBN and managing the subsequent situations effectively.

During the interviews, one medical educationist highlighted, “In our curriculum, there is a major gap when it comes to training doctors in breaking bad news. The focus is more on technical aspects, and soft skills like communication are often overlooked. This can lead to doctors struggling in delivering difficult news and navigating the emotional complexities that follow.“ [P1].

The participants also expressed concerns about the limited exposure and opportunities for doctors to stay up to date with constantly evolving medical knowledge. They emphasized the importance of continuous professional development to ensure doctors are equipped with the latest information and best practices in BBN effectively.

One interviewee shared, “It is crucial for doctors to have appropriate exposure to stay updated with the latest medical knowledge. Breaking bad news requires not only clinical expertise but also an understanding of the emotional and psychological aspects. Continuous professional development programs can help doctors refine their skills and keep abreast of the advancements in this field.” [P3].

Theme 3: learning by example and long-term impact of communication

The interviewees emphasized that BBN cannot be solely taught through theoretical instruction but should be demonstrated through practical examples and role modeling. They highlighted the significance of the communication process itself, as it can have long-term effects on the lives of patients and their families.

An interviewee emphasized, “It’s not just about teaching the process of breaking bad news; it’s about demonstrating empathy, active listening, and providing support throughout the entire journey. Learning by example and observing experienced doctors can be invaluable in developing the necessary communication skills. We must realize that the way we communicate with people during difficult times can have a profound impact on their well-being.” [P2].

Theme 4: lack of standardized training and guidelines

The medical educationists highlighted the absence of standardized training programs and guidelines specifically tailored to breaking bad news in Pakistan. They emphasized the need for a structured curriculum that includes comprehensive training modules and clear guidelines to ensure consistent and effective communication when delivering difficult news.

One interviewee stated, “There is a lack of standardized training and guidelines for breaking bad news in our medical education system. Without a structured curriculum and clear guidelines, doctors may face challenges in approaching these sensitive conversations. Establishing standardized training programs would provide doctors with the necessary tools and frameworks to navigate such situations effectively.” [P4].

Theme 5: inter-professional collaboration and team-based approach

The interviewees emphasized the importance of inter-professional collaboration and a team-based approach in BBN. They highlighted the need for effective communication and coordination among healthcare professionals, including doctors, nurses, psychologists, and social workers, to provide comprehensive support to patients and their families.

One medical educationist shared, “Breaking bad news is a complex process that requires a team-based approach. It is crucial for doctors to collaborate with other healthcare professionals, such as nurses, psychologists, and social workers, to ensure holistic care and support for patients and their families. Promoting effective inter-professional communication is essential in delivering sensitive news with empathy and addressing the diverse needs of patients.” [P7].

The present study aimed to explore the practices and training of clinicians in BBN to patients and their care givers in Pakistan. The combination of quantitative and qualitative findings, along with comparisons drawn from other studies conducted in developing countries, provides a comprehensive understanding of the current state of BBN practices and training in Pakistan and its relation to similar contexts.

Breaking bad news is part of the daily duties of almost all the clinicians. A study conducted in Sudan found that 56% of physicians had received training in BBN, indicating a relatively lower percentage compared to our study [ 15 ]. Similarly, a study from Ethiopia reported that 82% of participant physicians were not even aware of the SPIKES protocol, and 84% had no formal or informal training in BBN [ 8 ]. These findings suggest that the level of training and awareness regarding BBN varies across different developing countries. In our study revealed that only 9% of the participants reported receiving formal training specifically focused on BBN. This finding is consistent with studies conducted in other developing countries. For instance, a study from Lahore, Pakistan, involving postgraduate trainees, found a lack of knowledge and low satisfaction regarding BBN skills [ 16 ]. Similarly, a study in Peshawar, Pakistan, reported that 95% of participants had no training in BBN, highlighting a common gap in training among healthcare professionals [ 12 ]. Despite the fact that there is no formal training on BBN, the self-reported data in our study is quite positive.

The qualitative component of the study added valuable insights to complement the quantitative findings. Through in-depth interviews, participants’ experiences, perspectives, and challenges regarding BBN were explored. This approach provided a deeper understanding of the participants’ thoughts, emotions, and contextual factors influencing their communication practices. Themes and patterns emerged, offering a nuanced understanding of the quantitative results. The qualitative component also captured participants’ perceptions of training effectiveness, suggestions for improvement, and barriers to implementing optimal communication practices. Nonetheless, respondents were of the view that either at undergraduate or as part of the continuing education, inclusion of BBN training must be considered and that there should be a structured curriculum. However, there is an incongruent viewpoint too where some respondents said that skills of BBN come with experiential learning and maturity, and that it is about exhibiting one’s empathetic attitude and care during difficult times. This mixed methods approach allowed for a comprehensive examination of the research questions, generating practical implications for improving physician practices in breaking bad news [ 16 , 17 ].

Comparisons drawn from other developing countries also highlight the need for standardized training programs and guidelines for BBN. For instance, according to one research, adherence to the SPIKES protocol varied among participants, with 35–79% claiming to follow the protocol in routine practice [ 15 ]. Similarly, a study in Ethiopia found that a significant percentage of physicians were not complying with the guidelines of BBN [ 17 ]. These findings indicate the need for structured curricula and clear guidelines to ensure consistent and effective communication skills amongst doctors [ 18 ]. The importance of paying enough attention to the emotions of the recipient and the need to provide support after breaking bad news cannot be undermined at all [ 19 ]. A cultural shift is required within the medical profession and healthcare more generally so that BBN is viewed not merely as a soft skill but a professional responsibility for the doctor and a right for the patients and families who wish to have it [ 20 ].

Limitations

Our study has few limitations too. Very few participants were of the consultant cadre, most of the responded were junior doctors. Patients as well as the care givers are important stakeholders in this issue. Their views and perceptions were not explored in qualitative component of the study.

This study offers valuable insights into the practices and training of clinicians involved in BBN in Pakistan. Comparisons with other studies conducted in developing countries reveal both similarities and differences in BBN practices and training. The findings underscore the necessity of standardized training programs, formal guidelines, and improved communication skills education within medical curricula across developing nations. Recommendations arising from this study include integrating communication skills into the medical curriculum, developing standardized training programs, promoting continuous professional development, fostering inter-professional collaboration, and recognizing the importance of communication skills. By taking these steps, healthcare professionals will be equipped with the necessary tools to navigate the complexities of breaking bad news effectively and to provide compassionate care. Collaboration among medical institutions, policymakers, and regulatory bodies is essential to prioritize communication skills training, establish clear guidelines, and emphasize the value of empathetic and effective communication. Efforts should be directed towards increasing awareness, providing comprehensive training, and emphasizing the significance of effective communication when delivering difficult news, thus ensuring optimal patient care and support during challenging situations. Implementation of these recommendations will enhance the delivery of difficult news, increase patient satisfaction, and ensure comprehensive support during challenging times.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Biazar G, Delpasand K, Farzi F, Sedighinejad A, Mirmansouri A, Atrkarroushan Z. Breaking bad news: a valid concern among clinicians. Iran J Psychiatry. 2019;14(3):198–202.

PubMed PubMed Central Google Scholar

Ishaque S, Saleem T, Khawaja FB, Qidwai W. Breaking bad news: exploring patient’s perspective and expectations. J Pak Med Assoc. 2009;60(5):1–3.

Google Scholar

Servotte JC, Bragard I, Szyld D, Van Ngoc P, Scholtes B, Van Cauwenberge I, et al. Efficacy of a short role-play training on breaking bad news in the emergency department. West J Emerg Med. 2019;20(6):893–902.

Article PubMed PubMed Central Google Scholar

Baig L, Tanzil S, Ali SK, Shaikh S, Jamali S, Khan M. Breaking bad news: a contextual model for Pakistan. Pakistan J Med Sci. 2018;34(6):1336–40.

Ferreira Da Silveira FJ, Botelho CC, Valadão CC. Dando más notícias: a habilidade dos médicos em se comunicar com os pacientes. Sao Paulo Med J. 2017;135(4):323–31.

Article PubMed Google Scholar

Basheikh M. Preferences of the Saudi Population in breaking bad medical news: a regional study. Cureus. 2021;13(11).

Silva JV, Carvalho I. Physicians experiencing intense emotions while seeing their patients: what happens? Perm J. 2016 Summer;20(3):15–229.

Fisseha H, Mulugeta W, Kassu RA, Geleta T, Desalegn H. Perspectives of protocol based breaking bad news among medical patients and physicians in a teaching hospital, Ethiopia. Ethiop J Health Sci. 2020;30(6):1017–26.

Ferraz Gonçalves JA, Almeida C, Amorim J, Baltasar R, Batista J, Borrero Y, et al. Family physicians’ opinions on and difficulties with breaking bad news. Porto Biomed J. 2017;2(6):277–81.

Kumar M, Goyal S, Singh K, Pandit S, Sharma DN, Verma AK, et al. Breaking bad news issues: a survey among radiation oncologists. Indian J Palliat Care. 2009;15(1):61–6.

Setubal MSV, Antonio MÂRGM, Amaral EM, Boulet J. Improving perinatology residents’ skills in breaking bad news: a randomized intervention study. Rev Bras Ginecol E Obstet. 2018;40(3):137–46.

Article Google Scholar

Jameel A, Noor SM, Ayub S. Survey on perceptions and skills amongst postgraduate residents regarding breaking bad news at teaching hospitals in Peshawar, Pakistan. J Pak Med Assoc. 2012;62(8):585–9.

PubMed Google Scholar

Ali AA. Communication skills training of undergraduates. J Coll Physicians Surg Pak. 2013;23(1):10–5.

Byrne D. A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Qual Quant. 2022;56:1391–412.

Abdalazim Dafallah M, Ahmed Ragab E, Hussien Salih M, Nuri Osman W, Omer Mohammed R, Osman M, et al. Breaking bad news: awareness and practice among Sudanese doctors. AIMS Public Health. 2020;7(4):758–68.

Sarwar MZ, Rehman F, Fatima SM, Suhail M, Naqi SA. Breaking bad news skill of postgraduate residents of tertiary care hospital of Lahore, Pakistan: a cross-sectional survey. J Pak Med Assoc. 2019;69(5):695–9.

Ali Khawaja RD, Akhtar W, Khawaja A, Irfan H, Naeem M, Memon M. Patient communication in radiology: current status of breaking bad news among radiologists and radiology trainees in Pakistan. J Coll Physicians Surg Pakistan. 2013;23(10):761–3.

Tran TQ, Scherpbier AJJA, van Dalen J, Dung DV, Elaine PW. Nationwide survey of patients’ and doctors’ perceptions of what is needed in doctor - patient communication in a southeast Asian context. BMC Health Serv Res. 2020;20:946.

Jalali R, Jalali A, Jalilian M. Breaking bad news in medical services: a comprehensive systematic review. Heliyon. 2023;9(4):e14734.

O’Mahony S. Reframing the ‘difficult conversation’. J Royal Coll Physicians Edin. 2022;52(2):93–4.

Download references

Acknowledgements

Authors acknowledge the time given by the participants to answer our study questions and also for validating the transcripts.

No funding was obtained or used in this research.

Author information

Authors and affiliations.

Health Services Academy, Park Road, Chak Shahzad, Islamabad, 44000, Pakistan

Muhammad Ahmed Abdullah & Babar Tasneem Shaikh

Federal Polyclinic Hospital, Islamabad, Pakistan

Kashif Rehman Khan

Indus Healthcare Network, Islamabad, Pakistan

Muhammad Asif Yasin

You can also search for this author in PubMed Google Scholar

Contributions

MAA & BTS were involved in conception and design of the study; MAA, KRK and MAY did the data collection, analysis and interpretation of the literature; and later developed the first draft of the paper; BTS helped in triangulation and contributed in revising it critically for substantial intellectual content and for adding references. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Babar Tasneem Shaikh .

Ethics declarations

Ethics approval and consent to participate.

The researchers obtained ethical approval from the Institutional Review Board-Research Committee (IRB-RC) of Islamabad Medical & Dental College, ensuring compliance with ethical guidelines and safeguarding the rights and well-being of the study participants. Akbar Niazi Teaching Hospital is an affiliated teaching hospital of the Islamabad Medical & Dental College; whereas for the remaining hospitals separate letters were written and permission to conduct the study was sought. All participants were provided with information about the study objectives and procedures, and their informed consent was obtained prior to their inclusion in the research.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Abdullah, M.A., Shaikh, B.T., Khan, K.R. et al. Breaking bad news: A mix methods study reporting the need for improving communication skills among doctors in Pakistan. BMC Health Serv Res 24 , 588 (2024). https://doi.org/10.1186/s12913-024-11056-2

Download citation

Received : 31 January 2024

Accepted : 29 April 2024

Published : 06 May 2024

DOI : https://doi.org/10.1186/s12913-024-11056-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Breaking Bad News
Communication skills
Counselling
Physicians’ training, Pakistan

BMC Health Services Research

ISSN: 1472-6963

General enquiries: [email protected]

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

Publications
Our Methods
Short Reads
Tools & Resources

Read Our Research On:

International Surveys

Pew Research Center regularly conducts public opinion surveys in countries outside the United States as part of its ongoing exploration of attitudes, values and behaviors around the globe. To date, the Center has conducted more than 800,000 interviews in over 110 countries, mainly in conjunction with the longstanding Global Attitudes and Religion & Public Life projects but including others such as a 20-country international science study and another on digital connectivity in 11 emerging economies.

Country Specific Methodology

View detailed information such as mode of interview, sampling design, margin of error, and design effect, for each country we survey.

Cross-national studies constitute the bulk of Pew Research Center’s international survey research. Such studies pose special challenges when it comes to ensuring the comparability of data across multiple languages, cultures and contexts. To learn more about the challenges and best practices of polling in foreign countries and in multiple languages, see here.

Pew Research Center staff are responsible for the overall design and execution of each cross-national survey project, including topical focus, questionnaire development, countries to be surveyed and sample design. The Center’s staff frequently contract with a coordinating vendor to identify local, reputable research organizations, which are hired to collaborate on all aspects of sample and questionnaire design, survey administration and data processing. Both coordinating vendors and local research organizations are consulted on matters of sampling, fieldwork logistics, data quality and weighting. In addition, Pew Research Center often seeks the advice of subject matter experts and experienced survey researchers regarding the design and content of its cross-national studies.

Field periods for cross-national studies vary by country, study complexity and mode of administration, typically ranging from four to eight weeks. To the degree possible, Pew Research Center attempts to synchronize fieldwork across countries in order to minimize the chance that major events or developments might interfere with the comparability of results.

Pew Research Center’s cross-national studies are designed to be nationally representative using probability-based methods and target the non-institutional adult population (18 and older) in each country. The Center strives for samples that cover as much of the adult population as possible, given logistical, security and other constraints. Coverage limitations are noted in the detailed methods for each country.

Sample sizes for Pew Research Center’s cross-national studies are usually designed to yield at least 1,000 interviews, though larger samples may be required for more robust within-country comparisons. Reported margins of error account for design effects due to clustering, stratification and weighting.

In the case of both telephone and face-to-face surveys, weighting procedures correct for unequal selection probabilities as well as adjust to key sociodemographic distributions – such as gender, age and education – to align as closely as possible with reliable, official population statistics. To learn about the weighting approach used for U.S. surveys using the nationally representative online American Trends Panel, visit here .

Pew Research Center is a charter member of the American Association of Public Opinion Research (AAPOR) Transparency Initiative .

INTERNATIONAL SURVEYS

Other research methods, sign up for our weekly newsletter.

Fresh data delivered Saturday mornings

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 | Media Inquiries

Research Topics

Age & Generations
Coronavirus (COVID-19)
Economy & Work
Family & Relationships
Gender & LGBTQ
Immigration & Migration
International Affairs
Internet & Technology
Methodological Research
News Habits & Media
Non-U.S. Governments
Other Topics
Politics & Policy
Race & Ethnicity
Email Newsletters

ABOUT PEW RESEARCH CENTER Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

Terms & Conditions

Cookie Settings

Reprints, Permissions & Use Policy

Open access
Published: 06 May 2024

Lipids, apolipoproteins and gestational diabetes mellitus: a Mendelian randomization study

Dan Shan 1 ,
Ao Wang 1 &

BMC Pregnancy and Childbirth volume 24 , Article number: 347 ( 2024 ) Cite this article

Metrics details

This study investigates the causal relationship between lipid traits and GDM in an effort to better understand the aetiology of GDM.

Employing a two-sample Mendelian Randomization (MR) framework, we used Single Nucleotide Polymorphisms (SNPs) as instrumental variables to examine the impact of lipids and apolipoproteins on GDM. The research comprised univariable and multivariable MR analyses, with a prime focus on individual and combined effects of lipid-related traits. Statistical techniques included the fixed-effect inverse variance weighted (IVW) method and supplementary methods such as MR-Egger for comprehensive assessment.

Our findings revealed the following significant associations: apoA-I and HDL cholesterol were inversely correlated with GDM risk, while triglycerides showed a positive correlation. In multivariable analysis, apoA-I consistently exhibited a strong causal link with GDM, even after adjusting for other lipids and Body Mass Index (BMI).

The study demonstrates a significant causal relationship between apoA-I and GDM risk.

Peer Review reports

Introduction

Gestational Diabetes Mellitus (GDM) represents a major public health concern due to its increasing prevalence and profound effects on both maternal and foetal health [ 1 , 2 ]. Approximately 5–7% of pregnancies are estimated to be impacted by GDM, with variations depending on the population studied and diagnostic standards [ 3 ]. Characterised by glucose intolerance first identified during pregnancy, GDM is linked to an elevated risk of various adverse outcomes [ 4 ]. These include a higher likelihood of cesarean delivery, pre-eclampsia, and the development of type 2 diabetes in later life for mothers [ 5 , 6 , 7 ]. For infants, the risks extend to macrosomia, hypoglycaemia, and a predisposition to obesity [ 8 , 9 ].

Effective strategies for prevention, early detection, as well as management of GDM can mitigate short-term complications and offer a chance to improve long-term health outcomes [ 10 , 11 ]. This underscores the need for continued research into its pathophysiology, risk factors, and effective interventions. Environmental factors, lifestyle choices, and genetics all have a role in the pathophysiology of GDM [ 12 , 13 ]. Research into the role of lipid metabolism in GDM highlights its significance in the pathogenesis of this condition. Observational studies have demonstrated that dysregulated lipid profiles, including elevated triglycerides and low HDL cholesterol levels, are commonly observed in GDM. These lipid imbalances contribute to insulin resistance, a hallmark of GDM [ 14 ]. Additionally, a lot of attention has been given to the role of specific apolipoproteins, particularly Apolipoprotein A-I (apoA-I) and Apolipoprotein B(apoB), in modulating lipid metabolism and influencing GDM risk. Wu et al. found that apoA-I protects rats from pregnancy-induced insulin resistance by increasing insulin sensitivity and inhibiting inflammation in adipose tissue and skeletal muscle [ 15 ]. Zheng et al. reported that the serum levels of triglycerides, LDL cholesterol, and Apolipoprotein B during the first trimester of pregnancy have important clinical value in predicting GDM [ 16 ]. However, the causal nature of this association is yet unclear and requires further investigation.

Mendelian Randomization (MR) is a method that leverages genetic variations as tools to infer causal relationships between risk factors and diseases [ 17 ]. In MR studies, genetic variants known to affect lipid levels (such as those affecting HDL cholesterol, LDL cholesterol, and triglyceride levels) are employed as instrumental variables. These variants are generally unaltered by environmental factors and disease states, making them ideal for examining the causal effect of lipid levels on GDM risk. This robust methodology may provide valuable insights into the underlying mechanisms while shedding light on the biological pathways linking lipid-related traits to GDM.

Materials and methods

Study design.

In this research, we conducted a two-sample Mendelian randomization (MR) analysis in order to assess the causal link between lipids and apolipoproteins and GDM. SNPs served as instrumental variables (IVs) [ 18 ]. To enhance result accuracy, validating three key hypotheses throughout the entire process is crucial [ 19 ]. We identified genetic variants significantly associated with lipid levels and calculated the corresponding F-statistics to assess the strength of each variant as an instrumental variable. We conducted an analysis of confounding factors to ensure that the selected variants are not associated with known confounders, such as BMI. We also used methods such as MR-Egger regression to evaluate the potential pleiotropy of the genetic variants, further confirming that their effects on GDM are primarily mediated through lipid levels (Fig. 1 ).

Overview of the MR analysis process. Abbreviations: MR, mendelian randomization; IVs, instrumental variables; IVW, Inverse variance weighted; HDL-C, High density lipoprotein cholesterol; LDL-C, Low density lipoprotein cholesterol

The univariable MR analysis sought to analyse the correlation between specific lipid-related traits and GDM. The multivariable MR analysis, on the other hand, aimed to assess the individual impacts of interrelated lipid-related traits on GDM [ 20 ]. Both analyses aimed to comprehend the relationship between lipid-related traits and the risk of GDM, with the univariable focusing on individual traits and the multivariable concentrating on their interactions. All original studies obtained ethical review approval and informed consent. Genetic instruments for apoA-I, apoB, LDL cholesterol, HDL cholesterol, and triglycerides were extracted from the IEU Open GWAS database (Supplementary Table S1 ).

Statistical analyses

Our main approach for MR analysis was the fixed-effect inverse variance weighted (IVW) method. In cases where potential heterogeneity among selected SNPs was present, random effects modelling was employed [ 21 ]. Additionally, we utilised four other effective methods—MR-Egger, weighted median, weighted mode, and simple mode—to comprehensively analyse the potential relationship. It is noteworthy that although these methods offer a comprehensive evaluation, they might have less statistical power compared to the IVW test. We employed Cochran’s Q statistic and the MR-Egger test for assessing heterogeneity and pleiotropy, respectively.

Genetic instrument selection

In univariable MR analysis, independent SNPs linked to apoA-I, apoB, LDL cholesterol, HDL cholesterol, and triglycerides were isolated using a threshold of linkage disequilibrium clumping (r 2 = 0.001) and a window size of 10 megabases. Specifically, we focused on genome-wide significant SNPs ( p < 5e-8) associated with each trait so as to reduce redundancy.

Sensitivity analyses

To ensure the reliability of the identified causal effect of lipids and apolipoproteins on GDM, we carried out a thorough set of sensitivity analyses. Cochran’s Q statistic was utilised to assess potential heterogeneity within the data [ 22 ]. The MR-Egger intercept analysis was employed to investigate horizontal pleiotropy [ 23 ]. We also conducted a Leave-one-out analysis to examine if any single SNP substantially affected the outcomes by systematically removing SNPs individually. Additionally, reverse MR analyses were performed to explore the potential reverse causal link between lipids and apolipoproteins (as seen in the forward MR analysis) and GDM.

For multivariable MR analysis, we applied two models to further understand the connection between lipid-related traits and GDM risk. In Model 1, five lipid-related traits (apoA-I, apoB, LDL cholesterol, HDL cholesterol, and triglycerides) were included in multivariable analysis.

In Model 2, we included BMI for analysis, along with the three traits that showed positive associations in univariable analysis: apoA-I, HDL cholesterol, and triglycerides.

All analyses were performed using R (version 4.2.0) and RStudio, employing the R packages “TwoSampleMR” and “MR-PRESSO”.

Univariable Mendelian randomization analysis

After excluding SNPs associated with confounders, we identified 261 instrumental variables for apoA-I, 179 IVs for apoB, 86 IVs for HDL cholesterol, 147 IVs for LDL cholesterol, and 216 IVs for triglycerides. F-statistics of Instrument Variables for lipids and apolipoproteins are shown in Supplementary Table S7.

A significant correlation between apoA-I and the risk of GDM was determined through the IVW technique (OR [95%CI] = 0.76 [0.68–0.86]; p < 0.001). Moreover, HDL cholesterol was found to be significantly associated with a lower risk of GDM (OR [95%CI] = 0.79[0.69–0.89]; p < 0.001). Triglycerides were found to be significantly linked to an elevated risk of GDM (OR [95%CI] = 1.28[1.12–1.46]; p < 0.001). (Fig. 2 and Supplementary Table S3).

Univariable Mendelian randomization results using different methods. Abbreviations: SNP, Single nucleotide polymorphism; HDL-C, High density lipoprotein cholesterol; LDL-C, Low density lipoprotein cholesterol; OR, Odds ration; CI, Confidence interval

A reverse MR analysis was conducted to explore the potential causal effect of GDM on lipid-related traits. The findings suggested no reverse causal relationship between GDM and each trait (Supplementary Table S4).

Multivariable Mendelian randomization analysis

Figure 3 presents the outcomes of the multivariable MR analysis in model 1. When adjusting simultaneously for apoA-I, apoB, LDL cholesterol, HDL cholesterol, and triglycerides, apoA-I continued to have a strong causal link with GDM; the OR was 0.59 (95% CI = 0.38, 0.91). However, the effects for HDL cholesterol and triglycerides were greatly reduced (Supplementary Table S5).

Multivariable Mendelian randomization using the inverse-variance weighted method in model 1. Model 1 included Apolipoprotein A-I, Apolipoprotein B, LDL cholesterol, HDL cholesterol and triglycerides. Abbreviations: SNP, Single nucleotide polymorphism; HDL-C, High density lipoprotein cholesterol; LDL-C, Low density lipoprotein cholesterol; OR, Odds ration; CI, Confidence interval

Figure 4 exhibits the outcomes of the multivariable MR analysis in model 2. Body mass index is known as a risk factor for GDM. For model 2, the subjects included the three traits with positive results in univariable analysis (apoA-I, HDL cholesterol, and triglycerides) and BMI. When adjusting simultaneously for apoA-I, HDL cholesterol and triglycerides, and BMI, apoA-I consistently showed a strong causal association with GDM; the OR was 0.59 (95% CI = 0.38, 0.92). However, the estimates of HDL cholesterol and triglycerides were significantly reduced (Supplementary Table S6).

Multivariable Mendelian randomization using the inverse-variance weighted method in model 2. Model 2 included Apolipoprotein A-I, HDL cholesterol, triglycerides and Body mass index. Abbreviations: SNP, Single nucleotide polymorphism; HDL-C, High density lipoprotein cholesterol; OR, Odds ration; CI, Confidence interval

Sensitivity analysis

In our analysis of apoB and HDL cholesterol causal impacts on GDM, instrumental heterogeneity was detected (Cochran’s Q test, p < 0.05; Supplementary Table S2), leading us to employ the random-effects IVW method. On the other hand, for other analyses where no heterogeneity was observed (Cochran’s Q test, p > 0.05), the fixed-effects IVW method was applied.

There was no evidence of horizontal pleiotropy in the MR-Egger intercept analysis results. Scatter plots illustrated the causal effect of lipid-related traits on GDM across the five MR methods; a positive relationship is indicated by a slope greater than zero, and vice versa (Supplementary Figure S1 ). Furthermore, no discernible heterogeneity was shown by the Funnel plot symmetry (Supplementary Figure S2).

The incidence of gestational diabetes mellitus (GDM) is increasing worldwide and poses a major concern for the health of pregnant women and their fetuses [ 24 , 25 ]. Our comprehensive investigation into the role of lipids and apolipoproteins in GDM is essential because they play a key role in metabolic pathways that may have an important impact on pregnancy outcomes [ 26 ].

Our study explored the intricate interplay between lipids, apolipoproteins, and GDM. ApoA-I is the major protein component of HDL and plays a critical role in reverse cholesterol transport, a key process in removing cholesterol from tissues and returning it to the liver for excretion. Conversely, apoB is a primary component of LDL, very-low-density lipoprotein, and intermediate-density lipoprotein, which are involved in the transport of cholesterol and triglycerides from the liver to peripheral tissues.

The noteworthy associations revealed between these biomarkers and GDM provide novel insights into their potential roles in the pathogenesis of this condition. In the univariable Mendelian randomization analysis, compelling associations were discovered between lipid and apolipoprotein levels and the risk of GDM. Importantly, apoA-I has demonstrated an inverse correlation with GDM risk, suggesting its potential protective role. This is consistent with the established function of apoA-I in facilitating reverse cholesterol transport and its anti-inflammatory properties, which could potentially mitigate GDM risk through enhanced lipid metabolism as well as reduced inflammation [ 27 , 28 ]. Similarly, the inverse association between HDL cholesterol and the risk of GDM is indicative of the protective role of high-density lipoproteins in cardiovascular health, potentially exerting a similar influence on GDM by modulating lipid homeostasis and insulin sensitivity [ 29 , 30 ]. On the other hand, dysregulated triglyceride levels may increase vulnerability to GDM, as suggested by the positive connection found between triglycerides and GDM risk. This relationship highlights the effect of high triglyceride levels on insulin resistance and impaired glucose metabolism.

In multivariable Mendelian randomization analyses, two distinct models provided intriguing insights into the relationship between lipid profiles and gestational diabetes mellitus (GDM). Model 1, which encompassed adjustments for all pertinent lipid and apolipoprotein features, notably highlighted apoA-I’s sustained significant association with GDM. This reinforces the robustness of apoA-I’s impact on GDM risk independent of other lipid factors. Interestingly, although there were initial significant correlations between HDL cholesterol and triglycerides in the univariable analysis, their effects diminished in Model 1, suggesting a potential attenuation or mediation of their individual associations with GDM when adjusting for other lipid factors.

The critical role of apoA-I in GDM was further highlighted in Model 2 by the inclusion of BMI. Even after adjusting for BMI, apoA-I maintained a robust association with GDM, emphasising its independent contribution to GDM risk [ 31 ]. However, the effects of HDL cholesterol and triglycerides were notably attenuated in this adjusted model, suggesting a potential interplay between these lipid traits and BMI in influencing GDM susceptibility. These findings underscore apoA-I’s consistent and considerable relationship with GDM, irrespective of BMI adjustments, while also pointing to the need for deeper exploration into the complex interrelationships among lipids, BMI, and GDM susceptibility to gain a more comprehensive understanding of their collective impact.

Our study has identified a robust causal association between apoA-I and GDM, wherein elevated levels of apoA-I correspond to a significant reduction in GDM risk. This is partly in line with previous research. Metformin is a widely used insulin sensitizer [ 32 ]. As claimed by Karavia et al., the sensitizing effect of metformin is diminished in mice with apoA-I gene knock-down (apoA-I (-/-)), revealing that apoA-I may be involved in insulin sensitization [ 33 ]. A cross-sectional study found that low apoA-I was associated with insulin resistance in patients with impaired glucose tolerance [ 27 ]. However, Retnakaran et al. found no significant association between serum apoa-1 levels and the risk of insulin resistance or GDM in pregnant women in an observational study [ 34 ]. This discrepancy may be attributed to variations in study design and methodology, underlining the complexity involved in determining the precise role of apoA-I in GDM pathogenesis.

Our study uncovers a potential causal relationship between apoA-I levels and the risk of gestational diabetes, which could facilitate early prediction of GDM, inform prevention strategies and treatment interventions, and promote the advancement of personalized medicine.

It is important to note that our study has a number of limitations. Firstly, MR studies rely on certain assumptions, such as the absence of pleiotropy and horizontal pleiotropy, which could have an effect on the validity of the causal inference. While employing robust genetic instruments and sensitivity analyses to mitigate these concerns, complete elimination of residual confounding remains challenging. Secondly, our research also concentrated on the genetic effects of lipid-related traits on GDM risk. Although we adjusted for BMI in multivariable MR analysis, other factors, including environmental and lifestyle factors, were not taken into account. Subsequent studies should strive to incorporate these elements into their analyses, contributing to a more holistic comprehension of the causal mechanisms underlying the relationship between lipid-related traits and GDM. Thirdly, the summary statistics used in our study encompass data from both male and female participants and do not distinguish between lipid levels or BMI measured before and after pregnancy. This limitation may impact the specificity of our findings related to the risk of GDM, as the physiological conditions of these distinct groups can differ substantially. Additionally, a significant limitation of this study is the reliance on summary statistics, which restricts our ability to investigate non-linear relationships between lipid levels and the risk of GDM. The analysis operates under the assumption that these relationships are linear, which may not adequately capture the complexities inherent in lipid metabolism. This methodological simplification might fail to detect clinically significant non-linear effects, indicating that future research would benefit from employing more sophisticated methods capable of exploring these dynamics in greater detail.

In conclusion, our study strongly suggests a potential causal relationship between genetic susceptibility to apoA-I and a reduced risk of GDM. Further validation of our findings and investigation into the underlying biological mechanisms warrant additional research, which may advance personalised approaches to GDM prevention and management.

Availability of data and materials

Original data generated and analyzed during this study are included in this published article or supplementary material.

Abbreviations

Gestational Diabetes Mellitus

Apolipoprotein A-I

Apolipoprotein B

High-density lipoprotein cholesterol

Low-density lipoprotein cholesterol

Body mass index

Genome-wide association study

Mendelian randomization

Single nucleotide polymorphism

Instrumental variable

Inverse variance weighted

Mendelian randomization pleiotropy residual sum and outlier

Linkage disequilibrium

Odds ration

Confidence interval

High density lipoprotein

Low-density lipoprotein

Johns EC, Denison FC, Norman JE, Reynolds RM. Gestational diabetes mellitus: mechanisms, treatment, and complications. Trends Endocrinol Metab. 2018;29(11):743–54.

Article CAS PubMed Google Scholar

McIntyre HD, Catalano P, Zhang C, Desoye G, Mathiesen ER, Damm P. Gestational diabetes mellitus. Nat Rev Dis Primers. 2019;5(1):47.

Article PubMed Google Scholar

Sweeting A, Wong J, Murphy HR, Ross GP. A clinical update on gestational diabetes mellitus. Endocr Rev. 2022;43(5):763–93.

Article PubMed PubMed Central Google Scholar

Choudhury AA, Devi RV. Gestational diabetes mellitus - a metabolic and reproductive disorder. Biomed Pharmacother. 2021;143:112183.

Buchanan TA, Xiang AH, Page KA. Gestational diabetes mellitus: risks and management during and after pregnancy. Nat Rev Endocrinol. 2012;8(11):639–49.

Article CAS PubMed PubMed Central Google Scholar

Kim C. Maternal outcomes and follow-up after gestational diabetes mellitus. Diabet Med. 2014;31(3):292–301.

Yang Y, Wu N. Gestational diabetes mellitus and preeclampsia: correlation and influencing factors. Front Cardiovasc Med. 2022;9:831297.

Monteiro LJ, Norman JE, Rice GE, Illanes SE. Fetal programming and gestational diabetes mellitus. Placenta. 2016;48 Suppl 1:S54-s60.

Kautzky-Willer A, Harreiter J, Winhofer-Stöckl Y, Bancher-Todesca D, Berger A, Repa A, et al. Gestational diabetes mellitus (Update 2019). Wien Klin Wochenschr. 2019;131(Suppl 1):91–102.

Kintiraki E, Goulis DG. Gestational diabetes mellitus: multi-disciplinary treatment approaches. Metabolism. 2018;86:91–101.

Simmons D, Immanuel J, Hague WM, Teede H, Nolan CJ, Peek MJ, et al. Treatment of gestational diabetes mellitus diagnosed early in pregnancy. N Engl J Med. 2023;388(23):2132–44.

Plows JF, Stanley JL, Baker PN, Reynolds CM, Vickers MH. The pathophysiology of gestational diabetes mellitus. Int J Mol Sci. 2018;19(11):3342.

Hu G, Liu H, Leng J, Wang L, Li W, Zhang S, et al. Effects of a lifestyle intervention in young women with GDM and subsequent diabetes. Nutrients. 2022;14(24):5232.

Layton J, Powe C, Allard C, Battista MC, Doyon M, Bouchard L, et al. Maternal lipid profile differs by gestational diabetes physiologic subtype. Metabolism. 2019;91:39–42.

Wu BJ, Sun Y, Ong KL, Li Y, Tang S, Barter PJ, et al. Apolipoprotein A-I protects against pregnancy-induced insulin resistance in rats. Arterioscler Thromb Vasc Biol. 2019;39(6):1160–71.

Zheng Y, Hou W, Xiao J, Huang H, Quan W, Chen Y. Application value of predictive model based on maternal coagulation function and glycolipid metabolism indicators in early diagnosis of gestational diabetes mellitus. Front Public Health. 2022;10:850191.

Emdin CA, Khera AV, Kathiresan S. Mendelian randomization. JAMA. 2017;318(19):1925–6.

Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey SG. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27(8):1133–63.

Davies NM, Holmes MV, Davey SG. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601.

Sanderson E. Multivariable Mendelian randomization and mediation. Cold Spring Harb Perspect Med. 2021;11(2):984.

Article Google Scholar

Burgess S, Dudbridge F, Thompson SG. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Stat Med. 2016;35(11):1880–906.

Bowden J, Del Greco MF, Minelli C, Zhao Q, Lawlor DA, Sheehan NA, et al. Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption. Int J Epidemiol. 2019;48(3):728–42.

Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25.

Vince K, Perković P, Matijević R. What is known and what remains unresolved regarding gestational diabetes mellitus (GDM). J Perinat Med. 2020;48(8):757–63.

Giuliani C, Sciacca L, Biase ND, Tumminia A, Milluzzo A, Faggiano A, et al. Gestational diabetes mellitus pregnancy by pregnancy: early, late and nonrecurrent GDM. Diabetes Res Clin Pract. 2022;188:109911.

O’Malley EG, Reynolds CME, Killalea A, O’Kelly R, Sheehan SR, Turner MJ. Maternal obesity and dyslipidemia associated with gestational diabetes mellitus (GDM). Eur J Obstet Gynecol Reprod Biol. 2020;246:67–71.

Feng X, Gao X, Yao Z, Xu Y. Low apoA-I is associated with insulin resistance in patients with impaired glucose tolerance: a cross-sectional study. Lipids Health Dis. 2017;16(1):69.

Tang S, Tabet F, Cochran BJ, Cuesta Torres LF, Wu BJ, Barter PJ, et al. Apolipoprotein A-I enhances insulin-dependent and insulin-independent glucose uptake by skeletal muscle. Sci Rep. 2019;9(1):1350.

O’Mara AE, Johnson JW, Linderman JD, Brychta RJ, McGehee S, Fletcher LA, et al. Chronic mirabegron treatment increases human brown fat, HDL cholesterol, and insulin sensitivity. J Clin Invest. 2020;130(5):2209–19.

Chung ST, Katz LEL, Stettler-Davis N, Shults J, Sherman A, Ha J, et al. The relationship between lipoproteins and insulin sensitivity in youth with obesity and abnormal glucose tolerance. J Clin Endocrinol Metab. 2022;107(6):1541–51.

King TW, Cochran BJ, Rye KA. ApoA-I and diabetes. Arterioscler Thromb Vasc Biol. 2023;43(8):1362–8.

Madiraju AK, Qiu Y, Perry RJ, Rahimi Y, Zhang XM, Zhang D, et al. Metformin inhibits gluconeogenesis via a redox-dependent mechanism in vivo. Nat Med. 2018;24(9):1384–94.

Karavia EA, Hatziri A, Kalogeropoulou C, Papachristou NI, Xepapadaki E, Constantinou C, et al. Deficiency in apolipoprotein A-I ablates the pharmacological effects of metformin on plasma glucose homeostasis and hepatic lipid deposition. Eur J Pharmacol. 2015;766:76–85.

Retnakaran R, Ye C, Connelly PW, Hanley AJ, Sermer M, Zinman B. Serum apoA1 (Apolipoprotein A-1), insulin resistance, and the risk of gestational diabetes mellitus in human pregnancy-brief report. Arterioscler Thromb Vasc Biol. 2019;39(10):2192–7.

Download references

Acknowledgements

We sincerely thank the contributors in the openGWAS project.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and affiliations.

Key Laboratory of Obstetrics and Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, 610041, China

Dan Shan, Ao Wang & Ke Yi

You can also search for this author in PubMed Google Scholar

Contributions

Dan Shan and Ao Wang interpreted and analyzed the data, and drafted the manuscript. Dan Shan and Ke Yi concepted and designed the study and revised the manuscript. All authors contributed to and approved the final manuscript.

Corresponding author

Correspondence to Ke Yi .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary material 1., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Shan, D., Wang, A. & Yi, K. Lipids, apolipoproteins and gestational diabetes mellitus: a Mendelian randomization study. BMC Pregnancy Childbirth 24 , 347 (2024). https://doi.org/10.1186/s12884-024-06556-2

Download citation

Received : 22 January 2024

Accepted : 29 April 2024

Published : 06 May 2024

DOI : https://doi.org/10.1186/s12884-024-06556-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Apolipoproteins
Gestational diabetes mellitus

BMC Pregnancy and Childbirth

ISSN: 1471-2393

Submission enquiries: [email protected]
General enquiries: [email protected]

National Cancer Research Month: 7 CU Boulder discoveries that could improve, save lives

Share via Twitter
Share via Facebook
Share via LinkedIn
Share via E-mail

From developing new therapies to help patients cope with anxiety to discovering new ways to treat resistant breast cancer and new environmentally friendly methods for producing chemotherapy drugs, CU Boulder researchers are pushing boundaries in cancer research. In recognition of National Cancer Research Month, here’s a sampling of recent projects.

Geologists, biologists unearth the atomic fingerprints of cancer

Earth scientists have long turned to minute differences in hydrogen atoms to explore the ancient history of our planet. A new study suggests that these same tiny atoms could one day lead to new ways to track the growth of cancer.

Writing to wellness: New therapy helps cancer patients face biggest fears

With new medications extending the lives of advanced cancer patients, many live for years in the face of radical uncertainty. A new CU Boulder-born therapy has been shown to reduce trauma, depression, anxiety and fear.

How silencing a gene-silencer could lead to new cancer drugs

CU Boulder research reveals how a molecular machine known as PRC2 helps determine which cells become heart cells, versus brain or muscle or skin cells. The findings shed light on how development occurs and could pave the way for novel cancer treatments.

Why breast cancer survivors don’t take their meds, and what can be done about it

Hormone-blocking drugs can be life-saving for breast cancer survivors, reducing risk of recurrence by as much as 50%. Yet many patients stop taking them early or don’t take them as directed. CU Boulder research explores why, and what can be done about it.

When it comes to treating resistant breast cancer, 2 drugs may be better than 1

CU Boulder research shows that cancer cells can adapt in as little as one to two hours to new drugs called CDK2 inhibitors. The good news: Adding a second, widely available drug disables this workaround, squelching tumor growth.

New ‘magic beans’ produce ingredients for cancer treatments, vaccines, more

Tens of thousands of sharks are killed each year to harvest a key ingredient for vaccines, while old growth trees are slashed to obtain chemotherapy ingredients. Soybean farmer-turned molecular biologist Brian DeDecker has a better idea.

Breast cancer cells seen under the microscope

Researchers identify promising new target for drug-resistant breast and ovarian cancers

By inhibiting a protein that helps cancer cells repair themselves, scientists hope to develop new drugs that treat resistant tumors with fewer side effects.

News Headlines

Teen ‘Game Changers’ confronting youth violence crisis head-on

Two women and a man walk arm-in-arm down the street

Why do we move slower the older we get? New study delivers answers

Arts & Humanities
Business & Entrepreneurship
Climate & Environment
Education & Outreach
Health & Society
Law & Politics
Science & Technology

Campus Community

Administration
Announcements & Deadlines
Career Development
Getting Involved
Mind & Body

Events & Exhibits

Arts & Culture
Conferences
Lectures & Presentations
Performances & Concerts
Sports & Recreation
Workshops & Seminars

Subscribe to CUBT

Administrative eMemos

Buff Bulletin Board

Events Calendar

Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. Research design is important because it guides the entire research process and ensures that the study is conducted in a systematic and rigorous manner.

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

This type of research design is used to describe a phenomenon or situation. It involves collecting data through surveys, questionnaires, interviews, and observations. The aim of descriptive research is to provide an accurate and detailed portrayal of a particular group, event, or situation. It can be useful in identifying patterns, trends, and relationships in the data.

Correlational Research Design

Correlational research design is used to determine if there is a relationship between two or more variables. This type of research design involves collecting data from participants and analyzing the relationship between the variables using statistical methods. The aim of correlational research is to identify the strength and direction of the relationship between the variables.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This type of research design involves manipulating one variable and measuring the effect on another variable. It usually involves randomly assigning participants to groups and manipulating an independent variable to determine its effect on a dependent variable. The aim of experimental research is to establish causality.

Quasi-experimental Research Design

Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. This type of research design is used when it is not feasible or ethical to conduct a true experiment.

Case Study Research Design

Case study research design is used to investigate a single case or a small number of cases in depth. It involves collecting data through various methods, such as interviews, observations, and document analysis. The aim of case study research is to provide an in-depth understanding of a particular case or situation.

Longitudinal Research Design

Longitudinal research design is used to study changes in a particular phenomenon over time. It involves collecting data at multiple time points and analyzing the changes that occur. The aim of longitudinal research is to provide insights into the development, growth, or decline of a particular phenomenon over time.

Structure of Research Design

The format of a research design typically includes the following sections:

Introduction : This section provides an overview of the research problem, the research questions, and the importance of the study. It also includes a brief literature review that summarizes previous research on the topic and identifies gaps in the existing knowledge.
Research Questions or Hypotheses: This section identifies the specific research questions or hypotheses that the study will address. These questions should be clear, specific, and testable.
Research Methods : This section describes the methods that will be used to collect and analyze data. It includes details about the study design, the sampling strategy, the data collection instruments, and the data analysis techniques.
Data Collection: This section describes how the data will be collected, including the sample size, data collection procedures, and any ethical considerations.
Data Analysis: This section describes how the data will be analyzed, including the statistical techniques that will be used to test the research questions or hypotheses.
Results : This section presents the findings of the study, including descriptive statistics and statistical tests.
Discussion and Conclusion : This section summarizes the key findings of the study, interprets the results, and discusses the implications of the findings. It also includes recommendations for future research.
References : This section lists the sources cited in the research design.

Example of Research Design

An Example of Research Design could be:

Research question: Does the use of social media affect the academic performance of high school students?

Research design:

Research approach : The research approach will be quantitative as it involves collecting numerical data to test the hypothesis.
Research design : The research design will be a quasi-experimental design, with a pretest-posttest control group design.
Sample : The sample will be 200 high school students from two schools, with 100 students in the experimental group and 100 students in the control group.
Data collection : The data will be collected through surveys administered to the students at the beginning and end of the academic year. The surveys will include questions about their social media usage and academic performance.
Data analysis : The data collected will be analyzed using statistical software. The mean scores of the experimental and control groups will be compared to determine whether there is a significant difference in academic performance between the two groups.
Limitations : The limitations of the study will be acknowledged, including the fact that social media usage can vary greatly among individuals, and the study only focuses on two schools, which may not be representative of the entire population.
Ethical considerations: Ethical considerations will be taken into account, such as obtaining informed consent from the participants and ensuring their anonymity and confidentiality.

How to Write Research Design

Writing a research design involves planning and outlining the methodology and approach that will be used to answer a research question or hypothesis. Here are some steps to help you write a research design:

Define the research question or hypothesis : Before beginning your research design, you should clearly define your research question or hypothesis. This will guide your research design and help you select appropriate methods.
Select a research design: There are many different research designs to choose from, including experimental, survey, case study, and qualitative designs. Choose a design that best fits your research question and objectives.
Develop a sampling plan : If your research involves collecting data from a sample, you will need to develop a sampling plan. This should outline how you will select participants and how many participants you will include.
Define variables: Clearly define the variables you will be measuring or manipulating in your study. This will help ensure that your results are meaningful and relevant to your research question.
Choose data collection methods : Decide on the data collection methods you will use to gather information. This may include surveys, interviews, observations, experiments, or secondary data sources.
Create a data analysis plan: Develop a plan for analyzing your data, including the statistical or qualitative techniques you will use.
Consider ethical concerns : Finally, be sure to consider any ethical concerns related to your research, such as participant confidentiality or potential harm.

When to Write Research Design

Research design should be written before conducting any research study. It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.

Ideally, the research design should be developed as early as possible in the research process, before any data is collected. This allows the researcher to carefully consider the research question, identify the most appropriate research methodology, and plan the data collection and analysis procedures in advance. By doing so, the research can be conducted in a more efficient and effective manner, and the results are more likely to be valid and reliable.

Purpose of Research Design

The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection and analysis.

Some of the key purposes of research design include:

Providing a clear and concise plan of action for the research study.
Ensuring that the research is conducted ethically and with rigor.
Maximizing the accuracy and reliability of the research findings.
Minimizing the possibility of errors, biases, or confounding variables.
Ensuring that the research is feasible, practical, and cost-effective.
Determining the appropriate research methodology to answer the research question(s).
Identifying the sample size, sampling method, and data collection techniques.
Determining the data analysis method and statistical tests to be used.
Facilitating the replication of the study by other researchers.
Enhancing the validity and generalizability of the research findings.

Applications of Research Design

There are numerous applications of research design in various fields, some of which are:

Social sciences: In fields such as psychology, sociology, and anthropology, research design is used to investigate human behavior and social phenomena. Researchers use various research designs, such as experimental, quasi-experimental, and correlational designs, to study different aspects of social behavior.
Education : Research design is essential in the field of education to investigate the effectiveness of different teaching methods and learning strategies. Researchers use various designs such as experimental, quasi-experimental, and case study designs to understand how students learn and how to improve teaching practices.
Health sciences : In the health sciences, research design is used to investigate the causes, prevention, and treatment of diseases. Researchers use various designs, such as randomized controlled trials, cohort studies, and case-control studies, to study different aspects of health and healthcare.
Business : Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the impact of different business practices. Researchers use various designs, such as survey research, experimental research, and case studies, to study different aspects of the business world.
Engineering : In the field of engineering, research design is used to investigate the development and implementation of new technologies. Researchers use various designs, such as experimental research and case studies, to study the effectiveness of new technologies and to identify areas for improvement.

Advantages of Research Design

Here are some advantages of research design:

Systematic and organized approach : A well-designed research plan ensures that the research is conducted in a systematic and organized manner, which makes it easier to manage and analyze the data.
Clear objectives: The research design helps to clarify the objectives of the study, which makes it easier to identify the variables that need to be measured, and the methods that need to be used to collect and analyze data.
Minimizes bias: A well-designed research plan minimizes the chances of bias, by ensuring that the data is collected and analyzed objectively, and that the results are not influenced by the researcher’s personal biases or preferences.
Efficient use of resources: A well-designed research plan helps to ensure that the resources (time, money, and personnel) are used efficiently and effectively, by focusing on the most important variables and methods.
Replicability: A well-designed research plan makes it easier for other researchers to replicate the study, which enhances the credibility and reliability of the findings.
Validity: A well-designed research plan helps to ensure that the findings are valid, by ensuring that the methods used to collect and analyze data are appropriate for the research question.
Generalizability : A well-designed research plan helps to ensure that the findings can be generalized to other populations, settings, or situations, which increases the external validity of the study.

Research Design Vs Research Methodology

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

How to Cite Research Paper – All Formats and...

Data Collection – Methods Types and Examples

Delimitations in Research – Types, Examples and...

Research Paper Format – Types, Examples and...

Research Process – Steps, Examples and Tips

Institutional Review Board – Application Sample...

This paper is in the following e-collection/theme issue:

Published on 8.5.2024 in Vol 26 (2024)

Application of Patient-Reported Outcome Measurements in Adult Tumor Clinical Trials in China: Cross-Sectional Study

Authors of this article:

Original Paper

Yan Jia 1, 2 * ;
Qi Li 1, 2 * ;
Xiaowen Zhang 1 , MS ;
Yi Yan 3 ;
Shiyan Yan 4 , PhD ;
Shunping Li 5 , PhD ;
Wei Li 6 , PhD ;
Xiaowen Wu 7 , PhD ;
Hongguo Rong 1, 8 * , PhD ;
Jianping Liu 1, 8 , PhD

1 Center for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China

2 Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China

3 School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China

4 College of Acupuncture and Massage, Beijing University of Chinese Medicine, Beijing, China

5 Centre for Health Management and Policy Research, Shandong University, Shandong, China

6 International Research Center for Medicinal Administration, Peking University, Beijing, China

7 Peking University Cancer Hospital & Institute, Peking University, Beijng, China

8 Institute for Excellence in Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China

*these authors contributed equally

Corresponding Author:

Hongguo Rong, PhD

Center for Evidence-Based Chinese Medicine

Beijing University of Chinese Medicine

No. 11 Beisanhuan East Road, Chaoyang District

Beijing, 100029

Phone: 86 (10)64286757

Email: [email protected]

Background: International health policies and researchers have emphasized the value of evaluating patient-reported outcomes (PROs) in clinical studies. However, the characteristics of PROs in adult tumor clinical trials in China remain insufficiently elucidated.

Objective: This study aims to assess the application and characteristics of PRO instruments as primary or secondary outcomes in adult randomized clinical trials related to tumors in China.

Methods: This cross-sectional study identified tumor-focused randomized clinical trials conducted in China between January 1, 2010, and June 30, 2022. The ClinicalTrials.gov database and the Chinese Clinical Trial Registry were selected as the databases. Trials were classified into four groups based on the use of PRO instruments: (1) trials listing PRO instruments as primary outcomes, (2) trials listing PRO instruments as secondary outcomes, (3) trials listing PRO instruments as coprimary outcomes, and (4) trials without any mention of PRO instruments. Pertinent data, including study phase, settings, geographic regions, centers, participant demographics (age and sex), funding sources, intervention types, target diseases, and the names of PRO instruments, were extracted from these trials. The target diseases involved in the trials were grouped according to the American Joint Committee on Cancer Staging Manual, 8th Edition .

Results: Among the 6445 trials examined, 2390 (37.08%) incorporated PRO instruments as part of their outcomes. Within this subset, 26.82% (641/2390) listed PRO instruments as primary outcomes, 52.72% (1260/2390) as secondary outcomes, and 20.46% (489/2390) as coprimary outcomes. Among the 2,155,306 participants included in these trials, PRO instruments were used to collect data from 613,648 (28.47%) patients as primary or secondary outcomes and from 74,287 (3.45%) patients as coprimary outcomes. The most common conditions explicitly using specified PRO instruments included thorax tumors (217/1280, 16.95%), breast tumors (176/1280, 13.75%), and lower gastrointestinal tract tumors (173/1280, 13.52%). Frequently used PRO instruments included the European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire–30, the visual analog scale, the numeric rating scale, the Traditional Chinese Medicine Symptom Scale, and the Pittsburgh Sleep Quality Index.

Conclusions: Over recent years, the incorporation of PROs has demonstrated an upward trajectory in adult randomized clinical trials on tumors in China. Nonetheless, the infrequent measurement of the patient’s voice remains noteworthy. Disease-specific PRO instruments should be more effectively incorporated into various tumor disease categories in clinical trials, and there is room for improvement in the inclusion of PRO instruments as clinical trial end points.

Introduction

Patient-reported outcome (PRO) instruments are defined as any report regarding a patient’s health status obtained directly from the patient, excluding interpretation of the patient’s responses by clinicians or other individuals [ 1 ]. PRO data consist of information obtained directly from patients concerning their health status, symptoms, treatment adherence, physical and social functioning, health-related quality of life, and satisfaction with health care [ 2 - 4 ]. Serving as noninvasive, comprehensive, and patient-centered metrics, PROs play a pivotal role in enhancing patient engagement, facilitating informed clinical decisions, and improving patient-clinician communication [ 5 - 9 ]. High-quality PRO measures examined in rigorous trials can evaluate treatment effectiveness, assess patient adherence to treatment, guide drug research, and inform health care policies [ 2 , 5 ]. In addition, some PRO instruments could supplement safety data and contribute to the assessment of tolerability (eg, Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events [PRO-CTCAE]) [ 2 , 5 ].

In particular, PROs are valuable end points in trials of disabling, chronic, and incurable conditions because they systematically capture the patients’ perspectives in a scientifically rigorous way [ 3 , 10 , 11 ]. Recognizing their importance, clinical trials focused on tumors are increasingly incorporating PRO instruments as primary or secondary outcomes [ 12 - 15 ]. The European Commission has indicated the priority of preventing cancer and ensuring a high quality of life for patients with cancer within the framework of Europe’s Beating Cancer Plan [ 16 ]. The incorporation of PROs in clinical trials offers distinct advantages, including improvements in health-related quality of life, patient-clinician communication, and economic benefits from reduced health care use [ 17 - 20 ]. To uphold best practices in tumor clinical trials that use PROs, several methodological recommendations have emerged in recent years, such as SPIRIT-PRO (Standard Protocol Items: Recommendations for Interventional Trials–Patient-Reported Outcome), CONSORT-PRO (Consolidated Standards of Reporting Trials–Patient-Reported Outcome), SISAQOL (Setting International Standards in Analysing Patient-Reported Outcomes and Quality of Life Endpoints), and other relevant guidelines [ 2 - 4 , 21 ]. However, PRO measures often receive lower priority in the design of oncology-related clinical trials when compared to survival, imaging, and biomarker-related outcomes [ 22 ].

In China, PROs are increasingly being used in clinical trials, but there are challenges as well. A cross-sectional survey of interventional clinical trials conducted in China revealed that only 29.7% of the included trials listed PRO instruments as primary or secondary outcomes [ 23 ]. Moreover, there is a notable absence of comprehensive assessments evaluating the application of PRO instruments in tumor clinical trials in China. Unlike previous cross-sectional studies that encompassed all types of clinical trials, our study primarily examined adult tumor clinical trials in China that have listed PRO instruments as primary or secondary outcomes, referencing the methodologies and reporting patterns of a previous study [ 23 ]. We extracted the registration information of adult randomized clinical trials conducted in China to systematically analyze the application of PRO instruments in tumor clinical trials, aiming to evaluate the application of PRO instruments in adult tumor clinical trials in China and provide potential directions for further investigation.

Study Design

This cross-sectional study was designed to describe the characteristics of adult tumor clinical trials conducted in China between January 1, 2010, and June 30, 2022, that listed PRO instruments as primary or secondary outcomes. All clinical trials should be registered, and data of clinical trials were collected from 2 clinical trial registries, namely ClinicalTrials.gov and the Chinese Clinical Trial Registry, with public disclosure. We conducted data retrieval and export in July 2022. The clinical trials covered 34 provincial-level administrative regions in accordance with the 2019 version of China’s administrative divisions. We further sought to describe the PRO instruments frequently used in trials encompassing diverse target tumor conditions.

Data Collection Strategy

This study focused on interventional randomized clinical trials conducted in China involving participants aged ≥18 years ( Figure 1 ). Duplicate trials with 2 registration identification numbers were treated as a single trial (ClinicalTrials.gov records were retained). The evaluation of tumor clinical trials included three types of information: (1) basic information (registration number, registration date, scientific name, recruiting country, and other information), (2) key information (outcome, target disease, and age and sex of participants), and (3) characteristic information (main sponsor’s location, study settings, number of setting centers, study stage, funding source, and intervention type).

Data Classification

PRO instruments were defined by the US Food and Drug Administration in 2009 [ 1 ] as any report about a patient’s health status obtained directly from the patient, excluding interpretation of the patient’s response by clinicians or other individuals. Trials using PRO instruments as primary or secondary outcomes were considered PRO trials. On the basis of a previous study of PRO labeling of new US Food and Drug Administration–approved drugs (2016-2020) [ 24 ], eligible trials were classified into four groups: (1) trials that listed PRO instruments as primary outcomes, (2) trials that listed PRO instruments as secondary outcomes, (3) trials that listed PRO instruments as coprimary outcomes, and (4) trials without any mention of PRO instruments.

Statistical Analysis

Data related to the characteristics of the included trials (clinical phase, study setting, participant age and sex, region of the primary sponsor, setting center, number of PROs, funding source, and type of intervention) were extracted independently by 2 authors with a predesigned data extraction table. Owing to the varied categories and wide variation of target diseases, we classified similar target diseases based on classifications from the American Joint Committee on Cancer Staging Manual, 8th Edition ( Multimedia Appendix 1 ). On the basis of this categorization of diseases, we consolidated the PRO instruments used in each trial to identify those used most frequently. We conducted quantitative analysis only on items that listed the names of PRO instruments for a more detailed understanding of the commonly used evaluation tools. All data analyses were performed using Stata (version 14.0; StataCorp LLC).

Ethical Considerations

According to the Common Rule (45 CFR part 46) of the US Department of Health and Human Services (Office for Human Research Protections), this study is exempt from institutional review board approval and the requirement for informed patient consent because it did not involve clinical data or human participants. This study followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) reporting guidelines designed for observational studies in epidemiology.

Trial Characteristics

Table 1 presents a comprehensive overview of the included trials. The study included 7251 tumor-focused randomized controlled trials conducted in China between January 1, 2010, and June 30, 2022. Of these 7251 trials, 3276 (45.18%) were sourced from ClinicalTrials.gov, and 3975 (54.82%) were identified from the Chinese Clinical Trial Registry. Of these 7251 trials, after excluding 806 (11.12%) trials (n=5, 0.6% duplicates; n=465, 57.7% non-Chinese trials; n=321, 39.8% trials involving children; and n=15, 1.9% trials with incomplete reports), 6445 (88.88%) eligible trials were identified for analysis.

a The early phase trials included a clinical pretest as well as phase 0 and phase 1 trials.

b Diagnostic new technique clinical study, inspection technology, and trials involving multiple phases.

c Rehabilitation center, nursing home, campus, centers for disease control, home, and research institute.

d The trials were conducted in China, but their sponsor was based overseas.

e N/A: not applicable.

f Combination trials were funded partly by industry and partly by nonindustry institutions, such as universities, hospitals, and so on.

Of the 2,155,306 participants recruited in all included trials, 139,297 (6.46%) were involved in trials with PRO instruments as primary outcomes, 400,064 (18.56%) in trials with PRO instruments as secondary outcomes, and 74,287 (3.45%) in trials with PRO instruments as coprimary outcomes. Among the 6445 trials included, 2390 (37.08%) used PRO instruments as either primary or secondary outcomes, while 4055 (62.92%) did not use any PRO instrument.

The majority of the studies (6098/6445, 94.62%) did not impose any age restrictions on participants (children were excluded). In trials involving PROs, the proportion of older participants (aged >65 y; 42/2390, 1.78%) was slightly higher than in those without PROs (100/6445, 1.55%). Among all trials that incorporated PRO measurements, 17.15% (410/2390) included only female participants, while 4.48% (107/2390) included only male participants. Furthermore, in trials involving only female participants, the vast majority (974/1000, 97.4%) studied breast and female reproductive organ tumors. In trials exclusively involving male participants, more than half (135/267, 50.5%) centered around male genital organ tumors.

Regarding trial phases, of the 6445 clinical trials, early phase trials were the most prevalent (n=1317, 20.43%), followed by phase 3 trials (n=1004, 15.58%), phase 2 trials (n=873, 13.56%), and phase 4 trials (n=779, 12.09%). Of the 2390 PRO-related trials, early phase trials were again the most common (n=575, 24.06%), followed by phase 3 trials (n=284, 11.88%), phase 4 trials (n=269, 11.26%), and phase 2 trials (n=218, 9.12%).

Most of the trials (6034/6445, 93.62%) were conducted in hospitals, with hardly any (3/6445, 0.05%) conducted in community settings. More than half of the primary sponsors were located in eastern China (3745/6445, 58.11%), followed by northern (797/6445, 12.37%) and southern (682/6445, 10.58%) China, while 18.85% (1215/6445) of the primary sponsors were situated in other regions of China, such as the southwestern, central, northwestern, and northeastern regions. Similar patterns were observed for studies involving PROs. The majority of the major sponsors (1916/2390, 80.17%) originated from the eastern, northern, and southern regions of China, while 19.79% (473/2390) hailed from the southwestern, central, northeastern, and northwestern regions. There were differences in the proportions of PRO trials were noted among different provinces; the distribution of PRO instruments across Chinese provinces can be found in Multimedia Appendix 2 .

Moreover, 87.29% (5626/6445) of the trials were single-center trials, and only 11.11% (716/6445) were multicenter trials. Similar phenomena were observed for PRO-related studies, but multicenter trials accounted for a slightly higher percentage (312/2390, 13.05%). Of the 2390 PRO trials, 2144 (89.71%) used 1 to 3 PRO instruments, followed by 4 to 6 (n=218, 9.12%) and 7 to 9 (n=25, 1.05%) PRO instruments. The majority of the trials were nonindustry-funded trials (5443/6445, 84.45%), while 11.67% (752/6445) were industry-funded trials.

Table 2 shows the frequency of intervention types used across different trial classifications. The data indicated that more than a third of the included trials used drugs as the intervention (2496/6445, 38.73%), followed by combination therapies (1350/6445, 20.95%) and surgery (1044/6445, 16.2%). Among clinical trials involving drug interventions, nearly four-tenths (989/2496, 39.62%) used PRO instruments as their outcomes. Trials using drugs as the intervention exhibited a higher incidence of using PRO instruments as their primary or coprimary outcomes (468/989, 47.32%) compared to trials using other intervention types.

a PRO: patient-reported outcome.

b Other interventions included acupuncture, physical exercise, and psychosocial treatment.

Conditions and Participants

The annual counts of tumor clinical trials are listed in Figure 2 . During the study period—from January 1, 2010, to June 30, 2022—the number of tumor clinical trial registrations exhibited a consistent upward trajectory, paralleled by a commensurate increase in the number of clinical trials related to PROs.

Figures 3 and 4 depict the distribution of trial counts and corresponding participant numbers across different tumor types, respectively, wherein PROs served as outcomes. Among the 2390 tumor-related trials that used PRO instruments as primary or secondary outcomes, the top 5 tumors were thorax (448/2390, 18.74%), upper gastrointestinal tract (306/2390, 12.8%), lower gastrointestinal tract (300/2390, 12.55%), breast (289/2390, 12.09%), and head and neck (177/2390, 7.41%) tumors. Trials regarding female reproductive organ (168/2390, 7.03%) and hepatobiliary system (146/2390, 6.11%) tumors were also frequently observed. Male genital organ tumors (56/2390, 2.34%), central nervous system tumors (51/2390, 2.13%), endocrine system tumors (47/2390, 1.97%), and urinary tract tumors (33/2390, 1.38%) all accounted for proportions ranging from 1% to 5%, and hematologic malignant tumors (22/2390, 0.92%), neuroendocrine tumors (14/2390, 0.59%), bone tumors (8/2390, 0.33%), skin tumors (4/2390, 0.17%), ophthalmic tumors (2/2390, 0.08%), and soft tissue sarcoma (1/2390, 0.04%) constituted <1% of the trials.

Among the 613,648 participants enrolled in these PRO trials, 134,940 (22%) were diagnosed with lower gastrointestinal tract tumors, 131,470 (21.42%) with upper gastrointestinal tract tumors, and 79,068 (12.88%) with thorax tumors. Furthermore, there were a number of patients with breast tumors (63,238/613,648, 10.31%), female reproductive organ tumors (440,975/613,648, 6.68%), head and neck tumors (35,642/613,648, 5.81%), or hepatobiliary system tumors (22,044/613,648, 3.59%), each involving >10,000 patients. By contrast, conditions with <10,000 participants encompassed central nervous system tumors (8897/613,648, 1.45%), endocrine system tumors (8472/613,648, 1.38%), male genital organ tumors (8357/613,648, 1.36%), urinary tract tumors (6784/613,648, 1.11%), neuroendocrine tumors (3539/613,648, 0.58%), hematologic malignant tumors (2629/613,648, 0.43%), bone tumors (825/613,648, 0.13%), skin tumors (311/613,648, 0.05%), ophthalmic tumors (274/613,648, 0.04%), and soft tissue sarcoma (266/613,648, 0.04%).

PRO Instruments Used in Clinical Trials

Table 3 presents the number of explicitly specified PROs where trials precisely listed the names of the PRO instruments and the number of implicitly specified PROs where trials referenced patients’ subjective feelings without specifying the instruments used, separately for the 3 trial types. Specifically, the trial that specified the PRO instruments used was classified into “explicitly specified PROs,” and the trial that did not specify the instruments used was classified into “implicitly specified PROs.” It was evident that in primary and coprimary outcome trial sets, a greater number of trials explicitly listed the PRO instruments compared to those that did not specify the instruments used. Among the 3 trial types, the coprimary outcome category exhibited the highest proportion of explicitly specified PROs (339/489, 69.3%).

Tables 4 - 6 display the frequency of use of PRO scales for different diseases under the 3 categories. In trials using PRO instruments as coprimary outcomes, the visual analog scale (VAS) and the numeric rating scale (NRS) were the most commonly used scales for various tumors. For trials using PRO instruments as primary outcomes, the VAS was the most commonly used scale for various diseases. For trials using PRO instruments as secondary outcomes, the most commonly used scale for each disease was the European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire-30 (EORTC QLQ-C30).

a VAS: visual analog scale.

b NRS: numeric rating scale.

c EORTC QLQ-LC43: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Lung Cancer 43.

d SF-36: 36-item Short Form Health Survey.

e PSQI: Pittsburgh Sleep Quality Index.

f IPSS: International Prostate Symptom Score.

g LARS: Low Anterior Resection Syndrome.

h EORTC QLQ-C30: European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire-30.

i EORTC QLQ-STO22: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Stomach 22.

j UW-QOL: University of Washington Quality of Life Questionnaire.

k QoR-40: Quality of Recovery-40.

l IDS: Involvement-Detachment Scale.

m IIEF-15: International Index of Erectile Function-15.

n QoR-15: Quality of Recovery-15.

o TCMSS: Traditional Chinese Medicine Symptom Scale.

p N/A: not applicable.

a EORTC QLQ-C30: European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire-30.

b FACT-L: Functional Assessment of Cancer Therapy–Lung.

c EORTC QLQ-LC13: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Lung Cancer 13.

d FACT-B: Functional Assessment of Cancer Therapy–Breast.

e EORTC QLQ-BR23: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Breast Cancer 23.

f VAS: visual analog scale.

g EORTC QLQ-OES18: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Oesophageal Cancer 18.

h EORTC QLQ-H&N35: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Head and Neck Cancer 35.

i NRS: numeric rating scale.

j EORTC QLQ-CX24: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Cervical Cancer 24.

k EORTC QLQ-HCC18: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Hepatocellular Carcinoma 18.

l FACT-P: Functional Assessment of Cancer Therapy–Prostate.

m BPI-SF: Brief Pain Inventory–Short Form.

n FACT-G: Functional Assessment of Cancer Therapy–General.

o QoR-40: Quality of Recovery-40.

p SF-36: 36-item Short Form Health Survey.

q QoR-15: Quality of Recovery-15.

r WHOQOL-BREF: World Health Organization Quality of Life Brief Version.

s EORTC QLQ-PAN26: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Pancreatic Cancer 26.

t FACIT: Functional Assessment of Chronic Illness Therapy.

u HF-QOL: Hand-Foot Skin Reaction and Quality of Life.

v N/A: not applicable.

w EORTC QLQ-OPT30: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Ophthalmic Cancer 30.

c EORTC QLQ-C30: European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire-30.

d QoR-15: Quality of Recovery-15.

e TNSS: Total Nasal Symptom Score.

f BCS: Bruggemann Comfort Scale.

g PSQI: Pittsburgh Sleep Quality Index.

h ICIQ-SF: International Consultation on Incontinence Questionnaire–Short Form.

i FACT-P: Functional Assessment of Cancer Therapy–Prostate.

j HADS: Hospital Anxiety and Depression Scale.

k EORTC IADL-BN32: European Organisation for Research and Treatment of Cancer Instrumental Activities of Daily Living in Patients With Brain Tumors-32.

l N/A: not applicable.

m SAS: Self-Rating Anxiety Scale.

n SDS: Self-Rating Depression Scale.

To analyze the overall application of scales in explicitly specified PROs by condition, we examined the specific PRO instruments used in trials that explicitly mentioned the PRO instruments as primary or secondary outcomes ( Table 7 ). Of the 1280 trials, 321 (25.08%) used the EORTC QLQ-C30 ( Multimedia Appendix 3 ), which was the most commonly used PRO scale. Of note, the EORTC QLQ-C30 was the most commonly used scale in trials concerning lower gastrointestinal tract, upper gastrointestinal tract, head and neck, female reproductive organ, hepatobiliary system, bone, neuroendocrine, skin, and ophthalmic tumors as well as hematologic malignancies. In addition, the VAS was used in 24.77% (317/1280) of the trials ( Multimedia Appendix 3 ), predominating in trials involving thorax, breast, male genital organ, endocrine system, central nervous system, and urinary tract tumors. The NRS was also frequently used (169/1280, 13.2%) in cancer trials. More targeted scales have been used for different tumor diseases; for example, the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ)–Head and Neck Cancer 35 (36/101, 35.6%) was more common in head and neck tumor trials, the EORTC QLQ–Oesophageal Cancer 18 (15/140, 10.7%) and the EORTC QLQ–Stomach 22 (14/140, 10%) were frequently observed in upper gastrointestinal cancer trials, the EORTC QLQ–Colorectal Cancer 29 (14/173, 8.1%) scale was prevalent in lower gastrointestinal cancer trials, the EORTC QLQ–Hepatocellular Carcinoma 18 (8/67, 12%) was frequently found in hepatobiliary system tumor trials, the Functional Assessment of Cancer Therapy (FACT)–Lung (21/217, 9.7%) and the EORTC QLQ–Lung Cancer 13 (19/217, 8.8%) commonly featured in thorax tumor trials, the FACT–Breast (29/176, 16.5%) and the EORTC QLQ–Breast Cancer 23 (16/176, 9.1%) were frequently seen in breast cancer trials, the EORTC QLQ–Ovarian Cancer 28 (6/85, 7%) was a typical scale used in female reproductive organ tumor trials, the FACT–Prostate (7/31, 23%) was often used in male genital organ tumor trials, and the FACT–Anemia (1/9, 11%) and the FACT–Lymphoma (1/9, 11%) were common choices in hematologic malignant tumor trials.

b EORTC QLQ-C30: European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire-30.

c NRS: numeric rating scale.

d FACT-L: Functional Assessment of Cancer Therapy–Lung.

e EORTC QLQ-LC13: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Lung Cancer 13.

f FACT-B: Functional Assessment of Cancer Therapy–Breast.

g EORTC QLQ-BR23: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Breast Cancer 23.

h EORTC QLQ-CR29: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Colorectal Cancer 29.

i EORTC QLQ-OES18: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Oesophageal Cancer 18.

j EORTC QLQ-STO22: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Stomach 22.

k EORTC QLQ-H&N35: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Head and Neck Cancer 35.

l PG-SGA: Patient-Generated Subjective Global Assessment.

m SDS: Self-Rating Depression Scale.

n EORTC QLQ-OV28: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Ovarian Cancer 28.

o EORTC QLQ-HCC18: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Hepatocellular Carcinoma 18.

p TCMSS: Traditional Chinese Medicine Symptom Scale.

q FACT-P: Functional Assessment of Cancer Therapy–Prostate.

r BPI: Brief Pain Inventory.

s IPSS: International Prostate Symptom Score.

t QoR-15: Quality of Recovery-15.

u QoR-40: Quality of Recovery-40.

v PCSQ: Preparedness for Colorectal Cancer Surgery Questionnaire.

w WHOQOL-BREF: World Health Organization Quality of Life Brief Version.

x FACT-An: Functional Assessment of Cancer Therapy–Anemia.

y FACT-Lym: Functional Assessment of Cancer Therapy–Lymphoma.

z SF-36: 36-item Short Form Health Survey.

aa EORTC QLQ-PAN26: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Pancreatic Cancer 26.

ab N/A: not applicable.

ac HF-QoL: Hand-Foot Skin Reaction and Quality of Life.

ad EORTC QLQ-OPT30: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Ophthalmic Cancer 30.

ae PSQI: Pittsburgh Sleep Quality Index.

af BFI: Brief Fatigue Inventory.

Principal Findings

This cross-sectional study depicted the general characteristics of adult tumor clinical trials incorporating PROs in China and analyzed the application of PRO instruments in randomized clinical trials of tumors to provide potential directions for future research and serve as a reference for tumor clinical practice. The findings revealed that a significant proportion, specifically 62.92% (4055/6445) of the included trials, missed the opportunity to capture patients’ subjective evaluations. Of the trials with PRO instruments as end points, 26.82% (641/2390) used PRO instruments as primary outcomes, 52.72% (1260/2390) as secondary outcomes, and 20.46% (489/2390) as coprimary outcomes. The majority of PRO trials (2144/2390, 89.71%) used 1 to 3 PRO instruments. Given that PROs can authentically represent patients’ subjective experiences and evaluations, they should receive heightened emphasis in the context of tumor clinical trials. However, in light of the small proportion of tumor-related randomized clinical trials assessing PROs, policy makers and standard-setting bodies are recommended to further promote the collection of PROs in such trials in China.

This study delved into the yearly distribution of tumor clinical trials, indicating a notable surge in the use of PRO instruments as end points between January 1, 2010, and June 30, 2022. Among the trials incorporating PROs, early phase trials constituted the largest proportion (575/2390, 24.06%), followed by phase 3 (284/2390, 11.88%) and phase 4 (269/2390, 11.26%) trials. A retrospective cross-sectional study suggested a potential correlation between the use of PROs in late-stage trials and improved drug outcomes, such as overall survival [ 25 ]. However, the omission of PROs in late-stage trial results may reduce the value of patient participation in these trials. Previous work has shown that the concern regarding funding for PRO research seems significant, and additional funding was needed—and considered important—to pay for the use of PRO instruments to collect relevant data [ 26 ]. This may also be the reason why, among the included studies, there were few PRO tumor trials funded by industry. Relevant policies could provide more financial support for PRO tumor trials. In addition, our study indicated that the application of PRO instruments was more prevalent in trials involving drug interventions. PRO instruments can serve as valuable tools for assessing patient experiences during treatment, which is an essential aspect of drug discovery [ 27 ], and their absence can result in the exclusion of critical information, such as opportunities for patient-centered support programs and insights into benefit-risk profiles [ 27 ].

In accordance with prior research [ 23 ], our study also identified regional differences in the use of PROs. Tumor trials were more prevalent in the eastern, northern, and southern regions of China—especially in Shanghai, Beijing, Guangdong, and Jiangsu—and the adoption of PRO measurements followed a similar pattern. Conversely, in other regions of China, especially in the northwestern and northeastern regions—such as Qinghai, Tibet and Heilongjiang—both the overall number of tumor clinical trials and those incorporating PRO instruments as end points were conspicuously lower. These results indicated the relationship between the volume of tumor clinical trials and the adoption of PRO tools. In addition, other factors such as economic conditions and medical resources also played an important role in this phenomenon [ 28 ]. Relevant policies can continue to encourage medical resources to be tilted toward rural and less developed areas. Remarkably, the study suggested that in resource-constrained remote regions, simplified applications of PRO instruments may be considered in tumor clinical trials. Moreover, our investigation revealed a lower prevalence of industry-funded trials in tumor clinical trials in China. This discrepancy may be attributed to previous findings that tumor trials were characterized by increased risk and costliness [ 29 ].

This study further found that thorax tumors, breast tumors, and lower gastrointestinal tract tumors were the most common conditions in trials with explicit PRO instruments. This might be related to variances in tumor incidence and different clinical concerns [ 30 ]. In the primary and coprimary outcome trial sets, a higher proportion of trials explicitly listed the PRO instruments as end points compared to those not specifying PROs, underscoring the normative inclination to formalize the acquisition and application of PRO instruments. Adherence to guidelines and standardization of PRO application is essential to maximize the application of PRO trial data, enhance their impact, and minimize research waste [ 31 ]. In particular, studies have shown that the standardized PROs were conducive to making trials or clinical treatments more scientifically rigorous and ethically sound [ 32 - 35 ]. Therefore, the need to standardize the application of PRO instruments remains important, with an increased emphasis on explicitly specifying PRO instruments in clinical trials.

This study analyzed the frequency of the use of PRO instruments in different classifications of trials by medical condition and found that the VAS and the NRS were the most commonly used in trials where PROs were designated as coprimary outcomes. Meanwhile, in all trials that used PRO instruments as outcomes, the VAS and the NRS were consistently prevalent. This prevalence can be attributed to the precision, simplicity, and sensitivity of VAS scores, as well as the ease of use and standardized format of the NRS for assessing subjective indicators [ 36 - 38 ]. In addition, almost 90% of patients with cancer would experience pain during the course of their illness [ 39 ]. The pain is both prevalent and burdensome for patients, but there is a lack of objective evaluation indices available for this purpose [ 40 , 41 ]. Consequently, the VAS emerged as the preferred choice for pain assessment in clinical research. Similarly, the NRS, with its user-friendly nature and standardized format, has been the preferred tool for pain assessment [ 36 - 38 ]. PROs continue to represent the gold standard for evaluating patients’ core pain outcomes [ 42 - 44 ]. In this study, among the trials that used PRO instruments as secondary outcomes, the EORTC QLQ-C30 was the most commonly used (223/606, 36.8%), which might be attributed to the significance of addressing quality-of-life concerns for patients with tumors. This study also scrutinized the prevalent PRO instruments used in various medical conditions and found that the quality-of-life scale was frequently used in clinical trials involving tumors. The high frequency of the EORTC QLQ-C30 and FACT scale groups underscored the widespread application of these instruments in assessing patients’ quality of life in cancer clinical trials in China. Specific modules in the EORTC QLQ scale system, such as the EORTC QLQ–Breast Cancer 23, the EORTC QLQ–Lung Cancer 13, and the EORTC QLQ–Colorectal Cancer 29, have been widely used in various cancer diseases [ 45 , 46 ]. Similarly, specific modules in the FACT scales, such as FACT–Lung (lung cancer), FACT–Breast (breast cancer), and FACT–Prostate (prostate cancer), have exhibited a high rate of use in cancer clinical trials in China. The extensive use of various PRO scales indicates a growing awareness and acceptance of PRO instruments, which, in turn, encourages the development of more effective and reliable PRO instruments. PRO instruments can be divided into universal and disease-specific PRO instruments. Considering the heterogeneity of symptom types in patients with tumors, symptom assessment should be performed for specific diseases [ 47 ]. However, in different tumor trials, the explicitly specified PRO instruments were primarily quality-of-life scales, the VAS, and the NRS, suggesting a need for the application of disease-specific PRO scales for different tumor types in clinical trials. It is suggested that according to the heterogeneity of diseases, experts from different fields should be brought together to develop or improve the disease-specific scale through participatory and consensus approaches under the guidance of relevant guidelines [ 33 , 47 , 48 ]. Acceptance of the scale by a wide range of stakeholders would be beneficial to improve the quality and specificity of the scale [ 48 ]. Training of clinicians and researchers on disease-specific scales is recommended. In addition, regarding the implementation of PRO measurement, it can be attempted as part of routine clinical care delivery for corresponding diseases, as well as continuous quality improvement as a clinical care priority [ 48 ].

This study undertook an in-depth analysis of the fundamental aspects of tumor clinical trials encompassing PROs in China, involving categorizing tumors and assessing the application of specific PRO tools for each tumor type. The findings underscore the critical importance of integrating PRO measures into tumor clinical trials in China and the need to standardize the use of PRO instruments within these trials. In recent years, the Chinese government has attached great importance to the application of PRO instruments in clinical trials. To encourage the patient-centered concept of new drug development and make reasonable use of PRO instruments, the National Medical Products Administration formulated the Guiding Principles for the Application of Patient Reported Outcomes in Drug Clinical Research and Development in 2022. To further promote these guiding principles, the relevant departments can educate researchers about the importance of regulating the application of PRO instruments, provide an interpretation of these principles to researchers, and advise them to follow the guidelines. We encourage researchers to communicate relevant information to regulators in a timely manner to conduct higher-quality clinical trials, such as the background of the study, the type of study, and the scale used. Policy makers should further formulate and implement pertinent policies, and PRO application platforms need to be developed and promoted to accelerate rational use of PROs in tumor clinical trials. It is recommended to define or form an institution or department to coordinate and standardize the use of PROs in clinical trials [ 49 ]. The institution or department can provide researchers with some support, such as methodological guidance for PRO applications, interpretation of relevant guidelines, and guidance on internet technologies. Efforts should also be made to encourage communication and collaboration among policy makers, researchers, and medical institutions to promote the high-quality application of PROs in clinical trials. Furthermore, it is crucial to train clinicians in how to use PRO instruments in clinical practice. Ideally, this training can be part of standard medical education programs in the future. The most successful and effective way of training involved real patient cases and problem-based learning using audio and video clips, which could enable clinicians to know how to use PRO instruments and refer to the PRO data [ 50 ]. Researchers are encouraged to follow relevant guidelines and principles and actively engage in conducting high-quality tumor clinical trials to improve well-established PRO protocols and enrich the array of available PRO instruments, thereby advancing personalized population health. In addition, it is suggested to encourage and provide relevant support to patients who have difficulties in completing the PRO reports [ 51 ].

Limitations

It is important to acknowledge several limitations to this study. First, we excluded trials lacking detailed end point information, which may have introduced bias into the results. Second, the inclusion of trials that have not yet commenced participant recruitment, although necessary for our investigation, may have inflated the reported outcomes. Finally, the exclusion of trials involving children due to their limited expressive ability and the potential influence of parental reporting on outcomes may have introduced bias in the findings.

Conclusions

In China, the incorporation of PROs has demonstrated an upward trajectory in adult randomized clinical trials of tumors in recent years. Nonetheless, the infrequent measurement of the patient’s voice remains noteworthy. This study highlights the need for a more comprehensive evaluation of patients’ experiences in adult tumor clinical trials in China. The incorporation of patients’ subjective feelings in the context of tumor diseases is necessary. Disease-specific PRO instruments should be widely used in different categories of tumor disease. Pertinent policies should be formulated and implemented, and PRO application platforms need to be developed and promoted as well. In addition, researchers should actively engage in conducting high-quality tumor clinical trials. There is room for improvement in the standardization of PROs in China.

Acknowledgments

This work was supported by the high-level traditional Chinese Medicine Key Subjects Construction Project of the National Administration of Traditional Chinese Medicine—Evidence-Based Traditional Chinese Medicine (zyyzdxk-2023249).

Data Availability

The data sets generated and analyzed during this study are available from the corresponding author on reasonable request.

Authors' Contributions

HR and JL conceived of the presented idea. YJ and QL coordinated the data collection and analysis. XW, YY, and YJ performed the data extraction. YJ and QL wrote the first draft of the paper; and SY, SL, WL, and XW provided inputs for subsequent drafts. JL and HR provided comments related to the presentation of the findings and critically reviewed the manuscript. All authors read and approved the final manuscript.

Conflicts of Interest

None declared.

Classification of specific diseases.

The number of trials with patient-reported outcomes in each province of China.

Patient-reported outcome tests used most frequently.

Patrick DL, Burke LB, Powers JH, Scott JA, Rock EP, Dawisha S, et al. Patient-reported outcomes to support medical product labeling claims: FDA perspective. Value Health. 2007;10 Suppl 2:S125-S137. [ FREE Full text ] [ CrossRef ] [ Medline ]
Calvert M, Kyte D, Mercieca-Bebber R, Slade A, Chan AW, King MT, the SPIRIT-PRO Group, et al. Guidelines for inclusion of patient-reported outcomes in clinical trial protocols: the SPIRIT-PRO extension. JAMA. Feb 06, 2018;319(5):483-494. [ FREE Full text ] [ CrossRef ] [ Medline ]
Calvert M, Blazeby J, Altman DG, Revicki DA, Moher D, Brundage MD. Reporting of patient-reported outcomes in randomized trials: the CONSORT PRO extension. JAMA. Feb 27, 2013;309(8):814-822. [ CrossRef ] [ Medline ]
U.S. Department of Health and Human Services FDA Center for Drug Evaluation and Research, U.S. Department of Health and Human Services FDA Center for Biologics Evaluation and Research, U.S. Department of Health and Human Services FDA Center for Devices and Radiological Health. Guidance for industry: patient-reported outcome measures: use in medical product development to support labeling claims: draft guidance. Health Qual Life Outcomes. Oct 11, 2006;4:79. [ FREE Full text ] [ CrossRef ] [ Medline ]
The Lancet Neurology. Patient-reported outcomes in the spotlight. Lancet Neurol. Nov 2019;18(11):981. [ CrossRef ]
Marshall S, Haywood K, Fitzpatrick R. Impact of patient-reported outcome measures on routine practice: a structured review. J Eval Clin Pract. Oct 2006;12(5):559-568. [ CrossRef ] [ Medline ]
Greenhalgh J, Gooding K, Gibbons E, Dalkin S, Wright J, Valderas J, et al. How do patient reported outcome measures (PROMs) support clinician-patient communication and patient care? A realist synthesis. J Patient Rep Outcomes. Dec 2018;2:42. [ FREE Full text ] [ CrossRef ] [ Medline ]
Chan G, Bezuidenhout L, Walker L, Rowan R. The Impact on Life questionnaire: validation for elective surgery prioritisation in New Zealand prioritisation criteria in orthopaedic surgery. N Z Med J. Apr 01, 2016;129(1432):26-32. [ Medline ]
Black N. Patient reported outcome measures could help transform healthcare. BMJ. Jan 28, 2013;346:f167. [ FREE Full text ] [ CrossRef ] [ Medline ]
Tong A, Oberbauer R, Bellini MI, Budde K, Caskey FJ, Dobbels F, et al. Patient-reported outcomes as endpoints in clinical trials of kidney transplantation interventions. Transpl Int. May 20, 2022;35:10134. [ FREE Full text ] [ CrossRef ] [ Medline ]
Qian Y, Walters SJ, Jacques R, Flight L. Comprehensive review of statistical methods for analysing patient-reported outcomes (PROs) used as primary outcomes in randomised controlled trials (RCTs) published by the UK's Health Technology Assessment (HTA) journal (1997-2020). BMJ Open. Sep 06, 2021;11(9):e051673. [ FREE Full text ] [ CrossRef ] [ Medline ]
Dai W, Feng W, Zhang Y, Wang XS, Liu Y, Pompili C, et al. Patient-reported outcome-based symptom management versus usual care after lung cancer surgery: a multicenter randomized controlled trial. J Clin Oncol. Mar 20, 2022;40(9):988-996. [ FREE Full text ] [ CrossRef ] [ Medline ]
Kouzy R, Abi Jaoude J, Lin D, Nguyen ND, El Alam MB, Ludmir EB, et al. Patient-reported outcome measures in pancreatic cancer receiving radiotherapy. Cancers (Basel). Sep 02, 2020;12(9):2487. [ FREE Full text ] [ CrossRef ] [ Medline ]
Singhal U, Skolarus TA, Gore JL, Parry MG, Chen RC, Nossiter J, et al. Implementation of patient-reported outcome measures into health care for men with localized prostate cancer. Nat Rev Urol. May 2022;19(5):263-279. [ CrossRef ] [ Medline ]
Stover AM, Basch EM. Using patient-reported outcome measures as quality indicators in routine cancer care. Cancer. Feb 01, 2016;122(3):355-357. [ FREE Full text ] [ CrossRef ] [ Medline ]
Albreht T. Europe's beating cancer plan-a new step towards more comprehensive and equitable cancer control in Europe. Eur J Public Health. Jul 13, 2021;31(3):456-457. [ CrossRef ] [ Medline ]
Lizán L, Pérez-Carbonell L, Comellas M. Additional value of patient-reported symptom monitoring in cancer care: a systematic review of the literature. Cancers (Basel). Sep 15, 2021;13(18):4615. [ FREE Full text ] [ CrossRef ] [ Medline ]
Graupner C, Kimman ML, Mul S, Slok AH, Claessens D, Kleijnen J, et al. Patient outcomes, patient experiences and process indicators associated with the routine use of patient-reported outcome measures (PROMs) in cancer care: a systematic review. Support Care Cancer. Feb 2021;29(2):573-593. [ FREE Full text ] [ CrossRef ] [ Medline ]
Engstrom T, Tanner S, Lee WR, Forbes C, Walker R, Bradford N, et al. Patient reported outcome measure domains and tools used among adolescents and young adults with cancer: a scoping review. Crit Rev Oncol Hematol. Jan 2023;181:103867. [ CrossRef ] [ Medline ]
Yang LY, Manhas DS, Howard AF, Olson RA. Patient-reported outcome use in oncology: a systematic review of the impact on patient-clinician communication. Support Care Cancer. Jan 2018;26(1):41-60. [ CrossRef ] [ Medline ]
Coens C, Pe M, Dueck AC, Sloan J, Basch E, Calvert M, et al. International standards for the analysis of quality-of-life and patient-reported outcome endpoints in cancer randomised controlled trials: recommendations of the SISAQOL Consortium. Lancet Oncol. Feb 2020;21(2):e83-e96. [ CrossRef ]
Basch E, Geoghegan C, Coons SJ, Gnanasakthy A, Slagle AF, Papadopoulos EJ, et al. Patient-reported outcomes in cancer drug development and US regulatory review: perspectives from industry, the food and drug administration, and the patient. JAMA Oncol. Jun 01, 2015;1(3):375-379. [ CrossRef ] [ Medline ]
Zhou H, Yao M, Gu X, Liu M, Zeng R, Li Q, et al. Application of patient-reported outcome measurements in clinical trials in China. JAMA Netw Open. May 02, 2022;5(5):e2211644. [ FREE Full text ] [ CrossRef ] [ Medline ]
Gnanasakthy A, Norcross L, DeMuro Romano C, Carson RT. A review of patient-reported outcome labeling of FDA-approved new drugs (2016-2020): counts, categories, and comprehensibility. Value Health. Apr 2022;25(4):647-655. [ FREE Full text ] [ CrossRef ] [ Medline ]
Haslam A, Herrera-Perez D, Gill J, Prasad V. Patient experience captured by quality-of-life measurement in oncology clinical trials. JAMA Netw Open. Mar 02, 2020;3(3):e200363. [ FREE Full text ] [ CrossRef ] [ Medline ]
Watkins Bruner D, Bryan CJ, Aaronson N, Blackmore CC, Brundage M, Cella D, et al. Issues and challenges with integrating patient-reported outcomes in clinical trials supported by the National Cancer Institute–sponsored clinical trials networks. J Clin Oncol. Nov 10, 2007;25(32):5051-5057. [ CrossRef ]
Basch E, Dueck AC. Patient-reported outcome measurement in drug discovery: a tool to improve accuracy and completeness of efficacy and safety data. Expert Opin Drug Discov. Aug 2016;11(8):753-758. [ CrossRef ] [ Medline ]
Liniker E, Harrison M, Weaver JM, Agrawal N, Chhabra A, Kingshott V, et al. Treatment costs associated with interventional cancer clinical trials conducted at a single UK institution over 2 years (2009-2010). Br J Cancer. Oct 15, 2013;109(8):2051-2057. [ FREE Full text ] [ CrossRef ] [ Medline ]
Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics. Apr 01, 2019;20(2):273-286. [ FREE Full text ] [ CrossRef ] [ Medline ]
Cao W, Chen HD, Yu YW, Li N, Chen WQ. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020. Chin Med J (Engl). Mar 17, 2021;134(7):783-791. [ FREE Full text ] [ CrossRef ] [ Medline ]
Rivera SC, Kyte DG, Aiyegbusi OL, Slade AL, McMullan C, Calvert MJ. The impact of patient-reported outcome (PRO) data from clinical trials: a systematic review and critical analysis. Health Qual Life Outcomes. Oct 16, 2019;17(1):156. [ FREE Full text ] [ CrossRef ] [ Medline ]
Di Maio M, Gallo C, Leighl NB, Piccirillo MC, Daniele G, Nuzzo F, et al. Symptomatic toxicities experienced during anticancer treatment: agreement between patient and physician reporting in three randomized trials. J Clin Oncol. Mar 10, 2015;33(8):910-915. [ FREE Full text ] [ CrossRef ] [ Medline ]
Howell D, Molloy S, Wilkinson K, Green E, Orchard K, Wang K, et al. Patient-reported outcomes in routine cancer clinical practice: a scoping review of use, impact on health outcomes, and implementation factors. Ann Oncol. Sep 2015;26(9):1846-1858. [ FREE Full text ] [ CrossRef ] [ Medline ]
Crites JS, Chuang C, Dimmock A, Hwang W, Johannes B, Paranjape A, et al. PROs in the balance: ethical implications of collecting patient reported outcome measures in the electronic health record. Am J Bioeth. Mar 16, 2016;16(4):67-68. [ CrossRef ] [ Medline ]
Kyte D, Draper H, Calvert M. Patient-reported outcome alerts: ethical and logistical considerations in clinical trials. JAMA. Sep 25, 2013;310(12):1229-1230. [ CrossRef ] [ Medline ]
Jensen MP. The validity and reliability of pain measures in adults with cancer. J Pain. Feb 2003;4(1):2-21. [ FREE Full text ] [ CrossRef ] [ Medline ]
Caraceni A, Brunelli C, Martini C, Zecca E, De Conno F. Cancer pain assessment in clinical trials. A review of the literature (1999-2002). J Pain Symptom Manage. May 2005;29(5):507-519. [ FREE Full text ] [ CrossRef ] [ Medline ]
Hjermstad MJ, Fayers PM, Haugen DF, Caraceni A, Hanks GW, Loge JH, et al. Studies comparing Numerical Rating Scales, Verbal Rating Scales, and Visual Analogue Scales for assessment of pain intensity in adults: a systematic literature review. J Pain Symptom Manage. Jun 2011;41(6):1073-1093. [ FREE Full text ] [ CrossRef ] [ Medline ]
Hjermstad MJ, Fainsinger R, Kaasa S, European Palliative Care Research Collaborative (EPCRC). Assessment and classification of cancer pain. Curr Opin Support Palliat Care. Mar 2009;3(1):24-30. [ CrossRef ] [ Medline ]
Santoni A, Santoni M, Arcuri E. Chronic cancer pain: opioids within tumor microenvironment affect neuroinflammation, tumor and pain evolution. Cancers (Basel). Apr 30, 2022;14(9):2253. [ FREE Full text ] [ CrossRef ] [ Medline ]
Morishita S, Hirabayashi R, Tsubaki A, Aoki O, Fu JB, Onishi H, et al. Relationship between balance function and QOL in cancer survivors and healthy subjects. Medicine (Baltimore). Nov 19, 2021;100(46):e27822. [ FREE Full text ] [ CrossRef ] [ Medline ]
Comer SD, Zacny JP, Dworkin RH, Turk DC, Bigelow GE, Foltin RW, et al. Core outcome measures for opioid abuse liability laboratory assessment studies in humans: IMMPACT recommendations. Pain. Dec 2012;153(12):2315-2324. [ FREE Full text ] [ CrossRef ] [ Medline ]
Dworkin RH, Turk DC, McDermott MP, Peirce-Sandner S, Burke LB, Cowan P, et al. Interpreting the clinical importance of group differences in chronic pain clinical trials: IMMPACT recommendations. Pain. Dec 2009;146(3):238-244. [ CrossRef ] [ Medline ]
Younger J, McCue R, Mackey S. Pain outcomes: a brief review of instruments and techniques. Curr Pain Headache Rep. Feb 23, 2009;13(1):39-43. [ FREE Full text ] [ CrossRef ] [ Medline ]
Wan C, Meng Q, Yang Z, Tu X, Feng C, Tang X, et al. Validation of the simplified Chinese version of EORTC QLQ-C30 from the measurements of five types of inpatients with cancer. Ann Oncol. Dec 2008;19(12):2053-2060. [ FREE Full text ] [ CrossRef ] [ Medline ]
Aaronson NK, Ahmedzai S, Bergman B, Bullinger M, Cull A, Duez NJ, et al. The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst. Mar 03, 1993;85(5):365-376. [ CrossRef ] [ Medline ]
Taylor F, Reasner DS, Carson RT, Deal LS, Foley C, Iovin R, et al. Development of a symptom-based patient-reported outcome instrument for functional dyspepsia: a preliminary conceptual model and an evaluation of the adequacy of existing instruments. Patient. Oct 28, 2016;9(5):409-418. [ FREE Full text ] [ CrossRef ] [ Medline ]
Howell D, Fitch M, Bakker D, Green E, Sussman J, Mayo S, et al. Core domains for a person-focused outcome measurement system in cancer (PROMS-Cancer Core) for routine care: a scoping review and Canadian Delphi Consensus. Value Health. 2013;16(1):76-87. [ FREE Full text ] [ CrossRef ] [ Medline ]
Eton DT, Beebe TJ, Hagen PT, Halyard MY, Montori VM, Naessens JM, et al. Harmonizing and consolidating the measurement of patient-reported information at health care institutions: a position statement of the Mayo Clinic. Patient Relat Outcome Meas. Feb 10, 2014;5:7-15. [ FREE Full text ] [ CrossRef ] [ Medline ]
Santana MJ, Haverman L, Absolom K, Takeuchi E, Feeny D, Grootenhuis M, et al. Training clinicians in how to use patient-reported outcome measures in routine clinical practice. Qual Life Res. Jul 2015;24(7):1707-1718. [ FREE Full text ] [ CrossRef ] [ Medline ]
Riedl D, Lehmann J, Rothmund M, Dejaco D, Grote V, Fischer MJ, et al. Usability of electronic patient-reported outcome measures for older patients with cancer: secondary analysis of data from an observational single center study. J Med Internet Res. Sep 21, 2023;25:e49476. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by A Mavragani; submitted 14.01.23; peer-reviewed by Y Chu, L Guo; comments to author 24.10.23; revised version received 29.10.23; accepted 09.02.24; published 08.05.24.

©Yan Jia, Qi Li, Xiaowen Zhang, Yi Yan, Shiyan Yan, Shunping Li, Wei Li, Xiaowen Wu, Hongguo Rong, Jianping Liu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 08.05.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Alzheimer's disease & dementia
Arthritis & Rheumatism
Attention deficit disorders
Autism spectrum disorders
Biomedical technology
Diseases, Conditions, Syndromes
Endocrinology & Metabolism
Gastroenterology
Gerontology & Geriatrics
Health informatics
Inflammatory disorders
Medical economics
Medical research
Medications
Neuroscience
Obstetrics & gynaecology
Oncology & Cancer
Ophthalmology
Overweight & Obesity
Parkinson's & Movement disorders
Psychology & Psychiatry
Radiology & Imaging
Sleep disorders
Sports medicine & Kinesiology
Vaccination
Breast cancer
Cardiovascular disease
Chronic obstructive pulmonary disease
Colon cancer
Coronary artery disease
Heart attack
Heart disease
High blood pressure
Kidney disease
Lung cancer
Multiple sclerosis
Myocardial infarction
Ovarian cancer
Post traumatic stress disorder
Rheumatoid arthritis
Schizophrenia
Skin cancer
Type 2 diabetes
Full List »

share this!

May 7, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

fact-checked

reputable news agency

Study compares vestibular endolymphatic hydrops grading methods in Meniere disease

by Elana Gotkine

In a study published online April 17 in the European Archives of Oto-Rhino-Laryngology , different grading methods for vestibular endolymphatic hydrops (EH) and the severity of hearing loss are compared in Meniere disease (MD).

Zhihao Han, from the Beijing Friendship Hospital, and colleagues compared correlations between different grading methods of vestibular EH and the severity of hearing loss in MD in a retrospective study of 30 patients with MD. Patients underwent inner-ear magnetic resonance gadolinium-enhanced imaging using three-dimensional-real inversion recovery sequences and pure-tone audiometry. EH levels were evaluated according to classification methods outlined by Nakashima et al (M1), Fang et al (M2), Barath et al (M3), Liu et al (M4), and Bernaerts et al (M5).

The researchers found that compared with M1, interobserver consistency was superior for M2 to M5. A significant correlation was seen for the EH grading based on M4 and the average hearing thresholds at low-mid, high, and full frequencies and clinical stages. Correlations with some parameters were seen for M1, M2, M3, and M5. In terms of diagnostic efficiency for MD, M5 significantly outperformed M1, M2, M3, and M4 in a receiver operating characteristic curve analysis.

"These findings will assist clinicians in selecting an appropriate approach for the specific assessment of vestibular EH in MD," the authors write.

Explore further

Feedback to editors

Individuals of all ages with positive skin or blood test should receive preventive treatment for TB, new study says

8 hours ago

New research reports on financial entanglements between FDA chiefs and the drug industry

A 30-year US study links ultra-processed food to higher risk of early death

A third COVID vaccine dose improves defense for some clinically extremely vulnerable patients

Research team identifies four new genetic risk factors for multiple system atrophy

10 hours ago

About 90% of US adults are on the way to heart disease, study suggests

Researchers identify what drives PARP inhibitor resistance in advanced breast cancer

How infections influence our social empathy

11 hours ago

Health risks of using cannabis are higher in adolescents than in adults, study finds

12 hours ago

Study finds THC lingers in breastmilk with no clear peak point

Endolymphatic hydrops grade M2 found to correlate most strongly with hearing loss

Jun 1, 2023

Inner ear structural assignment method may identify Meniere disease

Oct 24, 2022

Morphology of vestibular aqueduct linked to Meniere disease

Mar 8, 2024

MRI can detect endolymphatic hydrops in Meniere disease

Apr 4, 2023

Gadolinium-enhanced MRI can differentiate Meniere from vestibular migraine

Aug 8, 2023

MRI techniques compared for assessing hydrops in Meniere disease

Sep 20, 2022

Recommended for you

New study offers insight into genesis of spina bifida

Serotonergic neuron findings suggest possible treatment for depression-related infertility

'Wraparound' implants represent new approach to treating spinal cord injuries

13 hours ago

Team develops computational approach to redesign antibodies for broader effectiveness against viral pandemics

14 hours ago

Let us know if there is a problem with our content

Use this form if you have come across a typo, inaccuracy or would like to send an edit request for the content on this page. For general inquiries, please use our contact form . For general feedback, use the public comments section below (please adhere to guidelines ).

Please select the most appropriate category to facilitate processing of your request

Thank you for taking time to provide your feedback to the editors.

Your feedback is important to us. However, we do not guarantee individual replies due to the high volume of messages.

E-mail the story

Your email address is used only to let the recipient know who sent the email. Neither your address nor the recipient's address will be used for any other purpose. The information you enter will appear in your e-mail message and is not retained by Medical Xpress in any form.

Newsletter sign up

Get weekly and/or daily updates delivered to your inbox. You can unsubscribe at any time and we'll never share your details to third parties.

More information Privacy policy

Donate and enjoy an ad-free experience

We keep our content available to everyone. Consider supporting Science X's mission by getting a premium account.

E-mail newsletter

IMAGES

Types of Research Methodology: Uses, Types & Benefits
15 Research Methodology Examples (2023)
Types of Research by Method
The scientific method is a process for experimentation
How to write a methods section of a research paper
Module 1: Introduction: What is Research?

VIDEO

The scientific approach and alternative approaches to investigation
Metho 6: The Research Process (Introduction)
WHAT RESEARCH METHOD AND DESIGN DID YOU USE IN THE STUDY?
PSY 2120: Why study research methods in psychology?
#MPC 005, BLOCK-1, UNIT-2 (Reliability) #IGNOU-#MAPC 1st Year
MGT648 Group Assignment

COMMENTS

Research Methods
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
Research Methods
Research methods are used in various fields to investigate, analyze, and answer research questions. Here are some examples of how research methods are applied in different fields: Psychology: Research methods are widely used in psychology to study human behavior, emotions, and mental processes. For example, researchers may use experiments ...
A tutorial on methodological studies: the what, when, how and why
Methodological studies - studies that evaluate the design, analysis or reporting of other research-related reports - play an important role in health research. They help to highlight issues in the conduct of research with the aim of improving health research methodology, and ultimately reducing research waste.
Research Methods--Quantitative, Qualitative, and More: Overview
The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more. This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will ...
What Is Research Methodology? Definition + Examples
What is research methodology? Research methodology simply refers to the practical "how" of a research study. More specifically, it's about how a researcher systematically designs a study to ensure valid and reliable results that address the research aims, objectives and research questions. Specifically, how the researcher went about deciding:
Research Methodology
Case Study Research Methodology. This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group. Action Research Methodology
Research Methods
Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. ... To situate your research in an existing body of work, or to evaluate trends within a research topic. Case study ...
Study designs: Part 1
Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem. Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the ...
A Practical Guide to Writing Quantitative and Qualitative Research
INTRODUCTION. Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses.1,2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results.3,4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the ...
Research Methods Guide: Research Design & Method
Most frequently used methods include: Observation / Participant Observation. Surveys. Interviews. Focus Groups. Experiments. Secondary Data Analysis / Archival Study. Mixed Methods (combination of some of the above) One particular method could be better suited to your research goal than others, because the data you collect from different ...
What are research methods?
A theoretical framework gives the researcher a specific "lens" to view the topic and think about it critically. it also serves as context to guide the entire study. This is a popular research method for analyzing works of literature, films, and other forms of media.
Research Methods: What are research methods?
What are research methods. Research methods are the strategies, processes or techniques utilized in the collection of data or evidence for analysis in order to uncover new information or create better understanding of a topic. There are different types of research methods which use different tools for data collection.
Research Methods In Psychology
Olivia Guy-Evans, MSc. Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.
Types of Research Designs Compared
But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study. Read more about creating a research design. Other interesting articles. If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other ...
Research
Research design: Research design refers to the overall plan and structure of the study, including the type of study (e.g., observational, experimental), the sampling strategy, and the data collection and analysis methods. Sampling strategy: Sampling strategy refers to the method used to select a representative sample of participants or units ...
Exploring Dating App Intimacies During COVID-19 in the UK: A Protocol
This protocol outlines a mixed-methods study to explore the dynamics of intimacy on dating apps before, during and after the crisis years of COVID-19 in the UK; how these influenced/were influenced by different gendered and sexual cultures of relating; and their impact on personal resilience, viral risk, and wellbeing.
Breaking bad news: A mix methods study reporting the need for improving
Background Effective skills and training for physicians are essential for communicating difficult or distressing information, also known as breaking bad news (BBN). This study aimed to assess both the capacity and the practices of clinicians in Pakistan regarding BBN. Methods A cross-sectional study was conducted involving 151 clinicians. Quantitative component used a structured questionnaire ...
How to Construct a Mixed Methods Research Design
This article provides researchers with knowledge of how to design a high quality mixed methods research study. To design a mixed study, researchers must understand and carefully consider each of the dimensions of mixed methods design, and always keep an eye on the issue of validity. We explain the seven major design dimensions: purpose ...
International Surveys
In addition, Pew Research Center often seeks the advice of subject matter experts and experienced survey researchers regarding the design and content of its cross-national studies. Field periods for cross-national studies vary by country, study complexity and mode of administration, typically ranging from four to eight weeks.
What Is Quantitative Research?
Quantitative research methods. You can use quantitative research methods for descriptive, correlational or experimental research. In descriptive research, you simply seek an overall summary of your study variables.; In correlational research, you investigate relationships between your study variables.; In experimental research, you systematically examine whether there is a cause-and-effect ...
Case Study
A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation. It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically ...
Lipids, apolipoproteins and gestational diabetes mellitus: a Mendelian
This study investigates the causal relationship between lipid traits and GDM in an effort to better understand the aetiology of GDM. Employing a two-sample Mendelian Randomization (MR) framework, we used Single Nucleotide Polymorphisms (SNPs) as instrumental variables to examine the impact of lipids and apolipoproteins on GDM. The research comprised univariable and multivariable MR analyses ...
National Cancer Research Month: 7 CU Boulder discoveries that could
From developing new therapies to help patients cope with anxiety to discovering new ways to treat resistant breast cancer and new environmentally friendly methods for producing chemotherapy drugs, CU Boulder researchers are pushing boundaries in cancer research.
Research Design
The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection ...
Journal of Medical Internet Research
Objective: This study aims to assess the application and characteristics of PRO instruments as primary or secondary outcomes in adult randomized clinical trials related to tumors in China. Methods: This cross-sectional study identified tumor-focused randomized clinical trials conducted in China between January 1, 2010, and June 30, 2022.
Study compares vestibular endolymphatic hydrops grading methods in
In a study published online April 17 in the European Archives of Oto-Rhino-Laryngology, different grading methods for vestibular endolymphatic hydrops (EH) and the severity of hearing loss are ...
What Is Qualitative Research?
Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. Qualitative research is the opposite of quantitative research, which involves collecting and ...

Secondary menu

Search form

About Research Methods

START HERE: SAGE Research Methods

Library Data Services at UC Berkeley

Other Data Services at Berkeley

General Research Methods Resources

Consultants

Related Resourcex

What Is Research Methodology? A Plain-Language Explanation & Definition (With Examples)

Research Methodology 101

What is research methodology?

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

What is sampling strategy?

What are data collection methods?

What are data analysis methods?

How do I choose a research methodology?

Example of a research methodology chapter

Psst... there’s more!

You Might Also Like:

199 Comments

Trackbacks/Pingbacks

Submit a Comment Cancel reply

Have a language expert improve your writing

Research Methods | Definition, Types, Examples

Table of contents

Qualitative vs quantitative data

Primary vs secondary data

Descriptive vs experimental data

Prevent plagiarism, run a free check.

Qualitative analysis methods

Quantitative analysis methods

Is this article helpful?

Research Methods Guide: Research Design & Method

Tutorial Videos: Research Design & Method

FAQ: Research Design & Method

Research Methodologies

What are research methods?

Research Methods: What are research methods?

What are research methods

Types of research

Data collection tools

Research Methods In Psychology

Experimental Design

Experimental Methods

Correlational Studies

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Interview Methods

Observations

Pilot Study

Research Design

Reliability

Meta-Analysis

Peer Review

Types of Data

Features of Science

Statistical Testing

Ethical Issues

Breaking bad news: A mix methods study reporting the need for improving communication skills among doctors in Pakistan

Introduction

Study design

Data collection

Sample size calculation

Data analysis and synthesis

Theme 1: ambiguity in subject domains and integration of communication skills

Theme 2: limited importance of breaking bad news in medical education

Theme 3: learning by example and long-term impact of communication

Theme 4: lack of standardized training and guidelines

Theme 5: inter-professional collaboration and team-based approach

Limitations

Data availability

Acknowledgements

Author information

Contributions

Corresponding author

Ethics declarations

Consent for publication

Competing interests

Additional information