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• v.9(4); Oct-Dec 2018

Study designs: Part 1 – An overview and classification

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

INTRODUCTION

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 nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

Classification of research study designs

• Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
• If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
• If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

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Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE: Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

• This is a collaborative and adaptive research design that lends itself to use in work or community situations.
• Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
• When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
• Action research studies often have direct and obvious relevance to improving practice and advocating for change.
• There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

• It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
• Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
• Personal over-involvement of the researcher may bias research results.
• The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
• Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA:  Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

• Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
• A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
• Design can extend experience or add strength to what is already known through previous research.
• Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
• The design can provide detailed descriptions of specific and rare cases.
• A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
• Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
• Design does not facilitate assessment of cause and effect relationships.
• Vital information may be missing, making the case hard to interpret.
• The case may not be representative or typical of the larger problem being investigated.
• If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
• Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

• Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
• The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
• Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
• Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
• Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

• A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
• Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
• The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
• Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field  [i.e., the act of triangulating the data].
• Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
• The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

• Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
• A well-reasoned and well-documented justification for identification and selection of the studies;
• Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
• Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
• Justification of the techniques used to evaluate the studies.
• Can be an effective strategy for determining gaps in the literature.
• Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
• Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
• Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
• Can be used to generate new hypotheses or highlight research problems for future studies.
• Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
• A large sample size can yield reliable, but not necessarily valid, results.
• A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
• Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

• Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
• Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
• Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
• Can provide a basis for applying ethical decision-making to practice.
• Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
• Brings clarity to general guiding practices and principles of an individual or group.
• Philosophy informs methodology.
• Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
• Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
• Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
• Limited application to specific research problems [answering the "So What?" question in social science research].
• Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
• While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
• There are limitations in the use of metaphor as a vehicle of philosophical analysis.
• There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

• The researcher has a limitless option when it comes to sample size and the sampling schedule.
• Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
• This is a useful design for exploratory studies.
• There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
• Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
• The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
• The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
• Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

• A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
• The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
• They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
• Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
• The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
• Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
• The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
• Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
• Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
• The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
• The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods .  David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research."  Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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Unsupervised Learning In Vision

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Lecture 12: Research Directions

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Lecture by Pieter Abbeel . Notes transcribed by James Le and Vishnu Rachakonda .

Of all disciplines, deep learning is probably the one where research and practice are closest together . Often, something gets invented in research and is put into production in less than a year. Therefore, it’s good to be aware of research trends that you might want to incorporate in projects you are working on.

Because the number of ML and AI papers increases exponentially, there’s no way that you can read every paper. Thus, you need other methods to keep up with research. This lecture provides a sampling of research directions, the overall research theme running across these samples, and advice on keeping up with the relentless flood of new research.

1 - Unsupervised Learning

Deep supervised learning, the default way of doing ML, works! But it requires so much annotated data. Can we get around it by learning with fewer labels? The answer is yes! And there are two major approaches: deep semi-supervised learning and deep unsupervised learning.

Deep Semi-Supervised Learning

Semi-supervised means half supervised, half unsupervised. Assuming a classification problem where each data point belongs to one of the classes, we attempt to come up with an intuition to complete the labeling for the unlabeled data points. One way to formalize this is: If anything is close to a labeled example, then it will assume that label. Thus, we can propagate the labels out from where they are given to the neighboring data points.

How can we generalize the approach above to image classification?

Xie et al. (2020) proposes Noisy Student Training :

First, they train a teacher model with labeled data.

Then, they infer pseudo-labels on the unlabeled data. These are not real labels, but those that they get from using the trained teacher model.

Even though these labels are not perfect (because they train on a small amount of labeled data), they can still see where they are more confident about those pseudo labels and inject those into their training set as additional labeled data.

When they retrain, they use dropout, data augmentation, and stochastic depth to inject noise into the training process. This enables the student model to be more robust and generalizable.

Deep Unsupervised Learning

Deep semi-supervised learning assumes that the labels in the supervised dataset are still valid for the unsupervised dataset. There’s a limit to the applicability because we assume that the unlabeled data is roughly from the same distribution as the labeled data .

With deep unsupervised learning, we can transfer the learning with multi-headed networks .

First, we train a neural network. Then, we have two tasks and give the network two heads - one for task 1 and another for task 2.

Most parameters live in the shared trunk of the network’s body. Thus, when you train for task 1 and task 2, most of the learnings are shared. Only a little bit gets specialized to task 1 versus task 2.

The key hypothesis here is that: For task 1 (which is unsupervised), if the neural network is smart enough to do things like predicting the next word in a sentence, generating realistic images, or translating images from one scale to another; then that same neural network is ready to do deep supervised learning from a very small dataset for task 2 (what we care about).

For instance, task 1 could be predicting the next word in a sentence, while task 2 could be predicting the sentiment in a corpus. OpenAI’s GPT-2 is the landmark result for next-word prediction where deep unsupervised learning could work. The results were so realistic, and there was a lot of press coverage. OpenAI deemed it to be too dangerous to be released at the time.

Furthermore, GPT-2 can tackle complex common sense reasoning and question answering tasks for various benchmarks. The table below displays those benchmarks where GPT-2 was evaluated on. The details of the tasks do not really matter. What’s more interesting is that: This is the first time a model, trained unsupervised on a lot of text to predict the next token and fine-tuned to specific supervised tasks, beats prior methods that might have been more specialized to each of these supervised tasks .

Another fascinating insight is that as we grow the number of model parameters, the performance goes up consistently. This means with unsupervised learning, we can incorporate much more data for larger models . This research funding inspired OpenAI to fundraise $1B for future projects to essentially have more compute available to train larger models because it seems like doing that will lead to better results. So far, that has been true ( GPT-3 performs better than GPT-2).

BERT is Google’s approach that came out around the same time as GPT-2. While GPT-2 predicts the next word or token, BERT predicts a word or token that was removed. In this task, the neural network looks at the entire corpus as it fills things back in, which often helps in later tasks (as the neural network has already been unsupervised-train on the entire text).

The table below displays BERT’s performance on the GLUE benchmark . The takeaway message is not so much in the details of these supervised tasks; but the fact that these tasks have a relatively small amount of labeled data compared to the unsupervised training that happens ahead of time. As BERT outperformed all SOTA methods, it revolutionized how natural language processing should be done.

BERT is one of the biggest updates that Google has made since RankBrain in 2015 and has proven successful in comprehending the intent of the searcher behind a search query.

Can we do the same thing for vision tasks? Let’s explore a few of them.

Predict A Missing Patch: A patch is high-dimensional, so the number of possibilities in that patch is very high (much larger than the number of words in English, for instance). Therefore, it’s challenging to predict precisely and make that work as well as in languages.

Solve Jigsaw Puzzles: If the network can do this, it understands something about images of the world. The trunk of the network should hopefully be reusable.

Predict Rotation: Here, you collect random images and predict what degree has been rotated. Existing methods work immensely well for such a task.

A technique that stood out in recent times is contrastive learning , which includes two variants - SimCLR (Chen et al., 2020) and MoCo (He et al., 2019). Here’s how you train your model with contrastive learning:

Imagine that you download two images of a dog and a cat from the Internet, and you don’t have labels yet.

You duplicate the dog image and make two versions of it (a greyscale version and a cropped version).

For these two dog versions, the neural network should bring them together while pushing the cat image far away.

You then fine-tune with a simple linear classifier on top of training completely unsupervised. This means that you must get the right features extracted from the images during training. The results of contrastive learning methods confirm that the higher the number of model parameters, the better the accuracy.

2 - Reinforcement Learning

Reinforcement learning (RL) has not been practical yet but nevertheless has shown promising results. In RL, the AI is an agent, more so than just a pattern recognizer. The agent acts in an environment where it is goal-oriented. It wants to achieve something during the process, which is represented by a reward function.

Compared to unsupervised learning, RL brings about a host of additional challenges:

Credit assignment: When the RL agent sees something, it has to take action. But it is not told whether the action was good or bad right away.

Stability: Because the RL agent learns by trial and error, it can destabilize and make big mistakes. Thus, it needs to be clever in updating itself not to destroy things along the way.

Exploration: The RL agent has to try things that have not been done before.

Despite these challenges, some great RL successes have happened.

DeepMind has shown that neural networks can learn to play the Atari game back in 2013. Under the hood is the Deep Q-Network architecture, which was trained from its own trial-and-error, looking at the score in the game to internalize what actions might be good or bad.

The game of Go was cracked by DeepMind - showing that the computer can play better than the best human player ( AlphaGo , AlphaGoZero , and AlphaZero ).

RL also works for the robot locomotion task. You don’t have to design the controller yourself. You just implement the RL algorithm ( TRPO , GAE , DDPG , PPO , and more) and let the agent train itself, which is a general approach to have AI systems acquire new skills. In fact, the robot can acquire such a variety of skills, as demonstrated in this DeepMimic work.

You can also accomplish the above for non-human-like characters in dynamic animation tasks. This is going to change how you can design video games or animated movies. Instead of designing the keyframes for every step along the way in your video or your game, you can train an agent to go from point A to point B directly.

RL has been shown to work on real robots .

BRETT (Berkeley Robot for the Elimination of Tedious Tasks) could learn to put blocks into matching openings in under an hour using a neural network trained from scratch. This technique has been used for NASA SuperBall robots for space exploration ideas.

A similar idea was applied to robotic manipulation solving Rubik’s cube , done at OpenAI in 2019. The in-hand manipulation is a very difficult robotic control problem that was mastered with RL.

CovariantAI

The fact that RL worked so well actually inspired Pieter and his former students (Tianhao Zhang, Rocky Duan, and Peter Chen) to start a company called Covariant in 2017. Their goal is to bring these advances from the lab into the real world. An example is autonomous order picking .

RL achieved mastery on many simulated domains. But we must ask the question: How fast is the learning itself? Tsividis et al., 2017 shows that a human can learn in about 15 minutes to perform better than Double DQN (a SOTA approach at the time of the study) learned after 115 hours.

How can we bridge this learning gap?

Based on the 2018 DeepMind Control Suite , pixel-based learning needs 50M more training steps than state-based learning to solve the same tasks. Maybe we can develop an unsupervised learning approach to turn pixel-level representations (which are not that informative) into a new representation that is much more similar to the underlying state.

CURL brings together contrastive learning and RL.

In RL, there’s typically a replay buffer where we store the past experiences. We load observations from there and feed them into an encoder neural network. The network has two heads: an actor to estimate the best action to take next and a critic to estimate how good that action would be.

CURL adds an extra head at the bottom, which includes augmented observations, and does contrastive learning on that. Similar configurations of the robot are brought closer together, while different ones are separated.

The results confirm that CURL can match existing SOTA approaches that learn from states and from pixels. However, it struggles in hard environments, with insufficient labeled images being the root cause.

4 - Meta Reinforcement Learning

The majority of fully general RL algorithms work well for any environments that can be mathematically defined. However, environments encountered in the real world are a tiny subset of all environments that could be defined. Maybe the learning takes such a long time because the algorithms are too general. If they are a bit more specialized in things they will encounter, perhaps the learning is faster.

Can we develop a fast RL algorithm to take advantage of this?

In traditional RL research, human experts develop the RL algorithm. However, there are still no RL algorithms nearly as good as humans after many years. Can we learn a better RL algorithm? Or even learn a better entire agent?

RL^2 ( Duan et al., 2016 ) is a meta-RL framework proposed to tackle this issue:

Imagine that we have multiple meta-training environments (A, B, and so on).

We also have a meta-RL algorithm that learns the RL algorithm and outputs a “fast” RL agent (from having interacted with these environments).

In the future, our agent will be in an environment F that is related to A, B, and so on.

Formally speaking, RL^2 maximizes the expected reward on the training Markov Decision Process (MDP) but can generalize to testing MDP. The RL agent is represented as a Recurrent Neural Network (RNN), a generic computation architecture where:

Different weights in the RNN mean different RL algorithms and priors.

Different activations in the RNN mean different current policies.

The meta-trained objective can be optimized with an existing “slow” RL algorithm.

The resulting RNN is ready to be dropped in a new environment.

RL^2 was evaluated on a classic Multi-Armed Bandit setting and performed better than provably (asymptotically) optimal RL algorithms invented by humans like Gittings Index, UCB1, and Thompson Sampling. Another task that RL^2 was evaluated on is visual navigation , where the agent explores a maze and finds a specified target as quickly as possible. Although this setting is maze-specific, we can scale up RL^2 to other large-scale games and robotic environments and use it to learn in a new environment quickly.

Schmidhuber. Evolutionary principles in self-referential learning . (1987)

Wiering, Schmidhuber. Solving POMDPs with Levin search and EIRA . (1996)

Schmidhuber, Zhao, Wiering. Shifting inductive bias with success-story algorithm, adaptive Levin search, and incremental self-improvement . (MLJ 1997)

Schmidhuber, Zhao, Schraudolph. Reinforcement learning with self-modifying policies (1998)

Zhao, Schmidhuber. Solving a complex prisoner’s dilemma with self-modifying policies . (1998)

Schmidhuber. A general method for incremental self-improvement and multiagent learning . (1999)

Singh, Lewis, Barto. Where do rewards come from? (2009)

Singh, Lewis, Barto. Intrinsically Motivated Reinforcement Learning: An Evolutionary Perspective (2010)

Niekum, Spector, Barto. Evolution of reward functions for reinforcement learning (2011)

Wang et al., (2016). Learning to Reinforcement Learn

Finn et al., (2017). Model-Agnostic Meta-Learning (MAML)

Mishra, Rohinenjad et al., (2017). Simple Neural AttentIve Meta-Learner

Frans et al., (2017). Meta-Learning Shared Hierarchies

5 - Few-Shot Imitation Learning

People often complement RL with imitation learning , which is basically supervised learning where the output is an action for an agent. This gives you more signal than traditional RL since for every input, you consistently have a corresponding output. As the diagram below shows, the imitation learning algorithm learns a policy in a supervised manner from many demonstrations and outputs the correct action based on the environment.

The challenge for imitation learning is to collect enough demonstrations to train an algorithm , which is time-consuming. To make the collection of demonstrations more efficient, we can apply multi-task meta-learning. Many demonstrations for different tasks can be learned by an algorithm, whose output is fed to a one-shot imitator that picks the correct action based on a single demonstration. This process is referred to as one-shot imitation learning ( Duan et al., 2017 ), as displayed below.

Conveniently, one-shot imitators are trained using traditional network architectures. A combination of CNNs, RNNs, and MLPs perform the heavy visual processing to understand the relevant actions in training demos and recommend the right action for the current frame of an inference demo. One example of this in action is block stacking .

Abbeel et al., (2008). Learning For Control From Multiple Demonstrations

Kolter, Ng. The Stanford LittleDog: A Learning And Rapid Replanning Approach To Quadrupled Locomotion (2008)

Ziebart et al., (2008). Maximum Entropy Inverse Reinforcement Learning

Schulman et al., (2013). Motion Planning with Sequential Convex Optimization and Convex Collision Checking

Finn, Levine. Deep Visual Foresight for Planning Robot Motion (2016)

6 - Domain Randomization

Simulated data collection is a logical substitute for expensive real data collection. It is less expensive, more scalable, and less dangerous (e.g., in the case of robots) to capture at scale. Given this logic, how can we make sure simulated data best matches real-world conditions?

Use Realistic Simulated Data

One approach is to make the simulator you use for training models as realistic as possible. Two variants of doing this are to carefully match the simulation to the world ( James and John, 2016 ; Johns, Leutenegger, and Division, 2016 ; Mahler et al., 2017 ; Koenemann et al., 2015 ) and augment simulated data with real data ( Richter et al., 2016 ; Bousmalis et al., 2017 ). While this option is logically appealing, it can be hard and slow to do in practice.

Domain Confusion

Another option is domain confusion ( Tzeng et al., 2014 ; Rusu et al., 2016 ).

In this approach, suppose you train a model on real and simulated data at the same time.

After completing training, a discriminator network examines the original network at some layer to understand if the original network is learning something about the real world.

If you can fool the discriminator with the output of the layer, the original network has completely integrated its understanding of real and simulated data.

In effect, there is no difference between simulated and real data to the original network, and the layers following the examined layer can be trained fully on simulated data.

Domain Randomization

Finally, a simpler approach called domain randomization ( Tobin et al., 2017 ; Sadeghi and Levine, 2016 ) has taken off of late. In this approach, rather than making simulated data fully realistic, the priority is to generate as much variation in the simulated data as possible. For example, in the below tabletop scenes, the dramatic variety of the scenes (e.g., background colors of green and purple) can help the model generalize well to the real world, even though the real world looks nothing like these scenes. This approach has shown promise in drone flight and pose estimation . The simple logic of more data leading to better performance in real-world settings is powerfully illustrated by domain randomization and obviates the need for existing variation methods like pre-training on ImageNet.

7 - Deep Learning For Science and Engineering

In other areas of this lecture, we’ve been focusing on research areas of machine learning where humans already perform well (i.e., pose estimation or grasping). In science and engineering applications, we enter the realm of machine learning performing tasks humans cannot. The most famous result is AlphaFold , a Deepmind-created system that solved protein folding, an important biological challenge. In the CASP challenge, AlphaFold 2 far outpaced all other results in performance. AlphaFold is quite complicated, as it maps an input protein sequence to similar protein sequences and subsequently decides the folding structure based on the evolutionary history of complementary amino acids.

Other examples of DL systems solving science and engineering challenges are in circuit design , high-energy physics , and symbolic mathematics .

AlphaFold: Improved protein structure prediction using potentials from deep learning . Deepmind (Senior et al.)

BagNet: Berkeley Analog Generator with Layout Optimizer Boosted with Deep Neural Networks . K. Hakhamaneshi, N. Werblun, P. Abbeel, V. Stojanovic. IEEE/ACM International Conference on Computer-Aided Design (ICAD), Westminster, Colorado, November 2019.

Evaluating Protein Transfer Learning with TAPE . R. Rao, N. Bhattacharya, N. Thomas, Y, Duan, X. Chen, J. Canny, P. Abbeel, Y. Song.

Opening the black box: the anatomy of a deep learning atomistic potential . Justin Smith

Exploring Machine Learning Applications to Enable Next-Generation Chemistry . Jennifer Wei (Google).

GANs for HEP . Ben Nachman

Deep Learning for Symbolic Mathematics . G. Lample and F. Charton.

A Survey of Deep Learning for Scientific Discovery . Maithra Raghu, Eric Schmidt.

As compute scales to support incredible numbers of FLOPs, more science and engineering challenges will be solved with deep learning systems. There has been exponential growth in the amount of compute used to generate the most impressive research results like GPT-3.

As compute and data become more available, we open a new problem territory that we can refer to as deep learning to learn . More specifically, throughout history, the constraint on solving problems has been human ingenuity. This is a particularly challenging realm to contribute novel results to because we’re competing against the combined intellectual might available throughout history. Is our present ingenuity truly greater than that of others 20-30 years ago, let alone 200-300? Probably not. However, our ability to bring new tools like compute and data most certainly is. Therefore, spending as much time in this new problem territory, where data and compute help solve problems , is likely to generate exciting and novel results more frequently in the long run.

“ Give a man a fish and you feed him for a day, teach a man to fish and you feed him for a lifetime ” (Lao Tzu)

Here are some tips on how to keep up with ML research:

(Mostly) don’t read (most) papers. There are just too many!

When you do want to keep up, use the following:

Tutorials at conferences: these capture the essence of important concepts in a practical, distilled way

Graduate courses and seminars

Yannic Kilcher YouTube channel

Two Minutes Paper Channel

The Batch by Andrew Ng

Import AI by Jack Clark

If you DO decide to read papers,

Follow a principled process for reading papers

Use Arxiv Sanity

AI/DL Facebook Group

ML Subreddit

Start a reading group: read papers together with friends - either everyone reads then discusses, or one or two people read and give tutorials to others.

Finally, should you do a Ph.D. or not?

You don’t have to do a Ph.D. to work in AI!

However, if you REALLY want to become one of the world’s experts in a topic you care about, then a Ph.D. is a technically deep and demanding path to get there. Crudely speaking, a Ph.D. enables you to develop new tools and techniques rather than using existing tools and techniques.

We are excited to share this course with you for free .

We have more upcoming great content. Subscribe to stay up to date as we release it.

We take your privacy and attention very seriously and will never spam you. I am already a subscriber

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Design and Directions for Research

Alister Cumming is Professor Emeritus and the former Head of the Centre for Educational Research on Languages and Literacies, University of Toronto, and since 2014, has been a Changjiang Scholar at Beijing Foreign Studies University. His research focuses on writing in second languages, language assessment, research methods, and program evaluation.

About the author

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Home » Future Research – Thesis Guide

Future Research – Thesis Guide

Table of Contents

Future Research

Definition:

Future research refers to investigations and studies that are yet to be conducted, and are aimed at expanding our understanding of a particular subject or area of interest. Future research is typically based on the current state of knowledge and seeks to address unanswered questions, gaps in knowledge, and new areas of inquiry.

How to Write Future Research in Thesis

Here are some steps to help you write effectively about future research in your thesis :

• Identify a research gap: Before you start writing about future research, identify the areas that need further investigation. Look for research gaps and inconsistencies in the literature , and note them down.
• Specify research questions : Once you have identified a research gap, create a list of research questions that you would like to explore in future research. These research questions should be specific, measurable, and relevant to your thesis.
• Discuss limitations: Be sure to discuss any limitations of your research that may require further exploration. This will help to highlight the need for future research and provide a basis for further investigation.
• Suggest methodologies: Provide suggestions for methodologies that could be used to explore the research questions you have identified. Discuss the pros and cons of each methodology and how they would be suitable for your research.
• Explain significance: Explain the significance of the research you have proposed, and how it will contribute to the field. This will help to justify the need for future research and provide a basis for further investigation.
• Provide a timeline : Provide a timeline for the proposed research , indicating when each stage of the research would be conducted. This will help to give a sense of the practicalities involved in conducting the research.
• Conclusion : Summarize the key points you have made about future research and emphasize the importance of exploring the research questions you have identified.

Examples of Future Research in Thesis

SomeExamples of Future Research in Thesis are as follows:

Future Research:

Although this study provides valuable insights into the effects of social media on self-esteem, there are several avenues for future research that could build upon our findings. Firstly, our sample consisted solely of college students, so it would be beneficial to extend this research to other age groups and demographics. Additionally, our study focused only on the impact of social media use on self-esteem, but there are likely other factors that influence how social media affects individuals, such as personality traits and social support. Future research could examine these factors in greater depth. Lastly, while our study looked at the short-term effects of social media use on self-esteem, it would be interesting to explore the long-term effects over time. This could involve conducting longitudinal studies that follow individuals over a period of several years to assess changes in self-esteem and social media use.

While this study provides important insights into the relationship between sleep patterns and academic performance among college students, there are several avenues for future research that could further advance our understanding of this topic.

• This study relied on self-reported sleep patterns, which may be subject to reporting biases. Future research could benefit from using objective measures of sleep, such as actigraphy or polysomnography, to more accurately assess sleep duration and quality.
• This study focused on academic performance as the outcome variable, but there may be other important outcomes to consider, such as mental health or well-being. Future research could explore the relationship between sleep patterns and these other outcomes.
• This study only included college students, and it is unclear if these findings generalize to other populations, such as high school students or working adults. Future research could investigate whether the relationship between sleep patterns and academic performance varies across different populations.
• Fourth, this study did not explore the potential mechanisms underlying the relationship between sleep patterns and academic performance. Future research could investigate the role of factors such as cognitive functioning, motivation, and stress in this relationship.

Overall, there is a need for continued research on the relationship between sleep patterns and academic performance, as this has important implications for the health and well-being of students.

Further research could investigate the long-term effects of mindfulness-based interventions on mental health outcomes among individuals with chronic pain. A longitudinal study could be conducted to examine the sustainability of mindfulness practices in reducing pain-related distress and improving psychological well-being over time. The study could also explore the potential mediating and moderating factors that influence the relationship between mindfulness and mental health outcomes, such as emotional regulation, pain catastrophizing, and social support.

Purpose of Future Research in Thesis

Here are some general purposes of future research that you might consider including in your thesis:

• To address limitations: Your research may have limitations or unanswered questions that could be addressed by future studies. Identify these limitations and suggest potential areas for further research.
• To extend the research : You may have found interesting results in your research, but future studies could help to extend or replicate your findings. Identify these areas where future research could help to build on your work.
• To explore related topics : Your research may have uncovered related topics that were outside the scope of your study. Suggest areas where future research could explore these related topics in more depth.
• To compare different approaches : Your research may have used a particular methodology or approach, but there may be other approaches that could be compared to your approach. Identify these other approaches and suggest areas where future research could compare and contrast them.
• To test hypotheses : Your research may have generated hypotheses that could be tested in future studies. Identify these hypotheses and suggest areas where future research could test them.
• To address practical implications : Your research may have practical implications that could be explored in future studies. Identify these practical implications and suggest areas where future research could investigate how to apply them in practice.

Applications of Future Research

Some examples of applications of future research that you could include in your thesis are:

• Development of new technologies or methods: If your research involves the development of new technologies or methods, you could discuss potential applications of these innovations in future research or practical settings. For example, if you have developed a new drug delivery system, you could speculate about how it might be used in the treatment of other diseases or conditions.
• Extension of your research: If your research only scratches the surface of a particular topic, you could suggest potential avenues for future research that could build upon your findings. For example, if you have studied the effects of a particular drug on a specific population, you could suggest future research that explores the drug’s effects on different populations or in combination with other treatments.
• Investigation of related topics: If your research is part of a larger field or area of inquiry, you could suggest potential research topics that are related to your work. For example, if you have studied the effects of climate change on a particular species, you could suggest future research that explores the impacts of climate change on other species or ecosystems.
• Testing of hypotheses: If your research has generated hypotheses or theories, you could suggest potential experiments or studies that could test these hypotheses in future research. For example, if you have proposed a new theory about the mechanisms of a particular disease, you could suggest experiments that could test this theory in other populations or in different disease contexts.

Advantage of Future Research

Including future research in a thesis has several advantages:

• Demonstrates critical thinking: Including future research shows that the author has thought deeply about the topic and recognizes its limitations. It also demonstrates that the author is interested in advancing the field and is not satisfied with only providing a narrow analysis of the issue at hand.
• Provides a roadmap for future research : Including future research can help guide researchers in the field by suggesting areas that require further investigation. This can help to prevent researchers from repeating the same work and can lead to more efficient use of resources.
• Shows engagement with the field : By including future research, the author demonstrates their engagement with the field and their understanding of ongoing debates and discussions. This can be especially important for students who are just entering the field and want to show their commitment to ongoing research.
• I ncreases the impact of the thesis : Including future research can help to increase the impact of the thesis by highlighting its potential implications for future research and practical applications. This can help to generate interest in the work and attract attention from researchers and practitioners in the field.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Building The ‘Bridge’ Between Research and Practice

• Posted May 20, 2024
• By Ryan Nagelhout

The way Doug Mosher tells the story, he didn't really come to the Harvard Graduate School of Education. HGSE came to him.

Mosher, Ph.D.’24, was working as a first-grade teacher at an underperforming elementary school in Nashville when a consultant came to introduce what he describes as “an awesome vocabulary intervention.”

The consultant, Claire White, Ed.M.’99, Ed.D.’05, was an Ed School alum whose goal was to help third- and fourth-grade students improve their language skills and reading vocabulary by discussing “controversial topics that are engaging,” says Mosher. White had worked with HGSE Professor Catherine Snow on the project and was now applying it in the field.

At first, his colleagues were reluctant to try something new, but Mosher was intrigued, and worked with White to modify the word generation lessons for his younger students. It was a “chance” brush with putting academic research into practice that changed the trajectory of his entire life.

“I feel so lucky to have been in that position,” says Mosher, a Ph.D. marshal for the HGSE class of 2024. “It just seemed fun, and I was at a point where I was looking for some new ideas to try in the classroom and this just seemed awesome.”

Mosher dove into the project for the next three years, helping White track student performance, collect data, and build lesson plans that he used in his own classroom. The program saw positive results, and soon the vocabulary intervention was implemented in other classrooms in the school. Mosher said he learned a lot, first and foremost that he really enjoyed doing academic research. And so when White told Mosher he could earn his doctorate doing this kind of work at HGSE – and maybe even get paid to do it – he was intrigued.

“I was just so excited about research. Having questions and designing things and then testing them out,” says Mosher. “I thought I was going to be a teacher forever. But I was starting to burn out. I was working really long hours. It’s a lot of pressure at an underperforming school to turn it around, and a lot of excitement. But at the same time, I was thinking I have to go back to school eventually.”

Teaching wasn’t exactly Mosher’s first love. A professional saxophonist, Mosher started substitute teaching when he moved to Nashville in the early 2000s. He learned to love the classroom, though, finding that same rush of energy and excitement he’d also experienced performing on stage.

Mosher applied to HGSE, particularly interested in the vocabulary research being done by Professor James Kim at the READS Lab, where he now conducts his own research. The three-part dissertation he defended this spring is a capstone of sorts, what Mosher describes as a shifting of his purpose in life.

“It’s been fun to see my true passion shift more toward research and working with schools and districts,” says Mosher. “Music will always be a part of my life, but I feel like this is my purpose now.”

That shift has changed how he views teachers, too. The learning environment at HGSE, he explains, is a big departure from the stereotypical music teacher myth that a “cold” and “suffering” teacher gets the most out of their students. Mosher called the faculty “a warm safety blanket” that created a welcoming learning environment over the last six years.

“It’s kind of what we try to do in intervention research,” says Mosher. “Create lessons that are engaging, build interest, build knowledge, make connections. That’s what all the faculty do.”

With Kim and the READS Lab, Mosher has worked on projects to improve reading comprehension in elementary school students using its Model of Reading Engagement (MORE) program. The project recently received a grant from the U.S. Department of Education to scale that model for use in new school districts. Mosher, always looking for chances to connect back with the classroom, describes the work as building “the bridge over the gap” that often exists between research and practice.

“Doug's exceptional research program shows how small improvements in the quality of teachers’ talk can have a big impact on students’ ability to read challenging science and social texts with greater understanding and engagement,” says Kim.

The work has certainly been noticed by the members of his cohort as well. Mosher calls his nomination to be a Ph.D. marshal “out of the blue.” He recalls the initial anxiety of joining a group of talented educators with experience working in so many impressive fields before arriving at HGSE. To be recognized by them, he says, reflects the support he’s felt from the community.

“I’m just very honored and touched that they voted me as a marshal,” says Mosher. “The cohort I’m in is full of really awesome, interesting, passionate people who are really dedicated to their areas of study. I was very surprised, but touched and honored.”

Mosher noted the difficulties his cohort experienced over the last six years, including a pandemic that disrupted research and entire ways of life. While some classmates moved away for good, Mosher doesn’t see himself leaving anytime soon.

“It feels like home,” says Mosher, whose father grew up in New England and has seen more family move to the area in recent years as well. “It's a really exciting thing to live in a place where I’ve always wanted to be. I finally ended up here and I don’t really want to leave.”

Mosher’s former school in Nashville, by the way, is now thriving. And here in Cambridge, so is he.

The latest research, perspectives, and highlights from the Harvard Graduate School of Education

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• Open access
• Published: 11 May 2024

Inotuzumab ozogamicin for the treatment of adult acute lymphoblastic leukemia: past progress, current research and future directions

• Nicholas J. Short 1 ,
• Elias Jabbour 1 ,
• Nitin Jain 1 &
• Hagop Kantarjian 1  

Journal of Hematology & Oncology volume  17 , Article number:  32 ( 2024 ) Cite this article

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Inotuzumab ozogamicin (INO) is an anti-CD22 antibody-drug conjugate that was first evaluated in B-cell lymphomas but was subsequently shown to be highly effective in acute lymphoblastic leukemia (ALL). INO improved response rates and survival in a randomized study in adults with relapsed/refractory B-cell ALL, leading to its regulatory approval in the United States in 2017. While the formal approval for INO is as monotherapy in relapsed/refractory ALL, subsequent studies with INO administered in combination with chemotherapy and/or blinatumomab both in the frontline and salvage settings have yielded promising results. In this review, we discuss the clinical development of INO in ALL, highlighting lessons learned from the initial clinical trials of INO, as well as the many ongoing studies that are seeking to expand the role of INO in ALL.

Introduction

The anti-CD22 antibody drug conjugate inotuzumab ozogamicin (INO) was developed in the early 2000s based on initial preclinical data showing promising activity in B-cell lymphoid diseases. These laboratory observations were then followed by several early phase clinical trials that showed significant efficacy of INO in acute lymphoblastic leukemia (ALL), ultimately prompting to its evaluation in a large, randomized trial in adults with relapsed/refractory CD22-positive B-cell ALL. In the pivotal INO-VATE study, INO significantly improved response rates and overall survival (OS) compared with conventional chemotherapy, leading to its approval by the Food and Drug Administration (FDA) in August 2017 [ 1 ]. Figure  1 shows a timeline of its clinical development. In this review, we discuss the lessons learned during its development and how these are being applied to current research efforts. We will also discuss the new research that is attempting to expand the potential applications of INO in B-cell ALL, including using it in combination with chemotherapy and/or other immunotherapies, in the frontline treatment of ALL, and in treatment of measurable residual disease (MRD).

Timeline of the clinical development of inotuzumab ozogamicin in acute lymphoblastic leukemia. For context, approval dates for other novel immunotherapies in adult B-cell acute lymphoblastic leukemia are also shown

Drug mechanism and preclinical development

INO is an IgG anti-CD22 monoclonal antibody drug conjugate that was developed by Celltech (a British biotechnology company) and Wyeth (a pharmaceutical company, later purchased by Pfizer in 2009). It is covalently linked to calicheamicin dimethyl hydrazide with acid-labile 4-(4’-acetylphenoxy) butanoic acid liner [ 2 ]. INO has sub-nanomolar binding affinity to CD22 and is rapidly internalized upon binding, after which it delivers the calicheamicin toxin intracellularly where it binds to the minor DNA groove and leads to double-strand cleavage and subsequent apoptosis. INO was first shown in preclinical studies to be active against B-cell lymphoma cell lines [ 2 ]. Subsequent studies were performed in mouse models of aggressive B-cell lymphomas, showing both monotherapy activity as well as synergy with rituximab or chemotherapy, including CVD and CHOP [ 3 , 4 , 5 ]. Given the clear preclinical activity in B-cell lymphoma models, INO was also tested in CD22-positive ALL models, where it induced complete tumor regression and cures in mice, warranting its clinical development in ALL [ 5 , 6 ].

Phase I and II studies

The first study of INO in humans was a phase I study in adults with relapsed or refractory CD22-positive B-cell non-Hodgkin’s lymphoma [ 7 ]. Seventy-nine patients were treated, and the maximum tolerated dose (MTD) was 1.8 mg/m 2 administered as a single dose every 3–4 weeks. Thrombocytopenia was the dose-limiting toxicity, with 90% of patients experiencing thrombocytopenia of any grade, which was grade ≥ 3 in 63%. Encouraging activity was observed, and the overall response rate was 39% among all patients, with response rates in follicular lymphoma and diffuse large B-cell lymphoma of 69% and 15%, respectively, at the MTD. Investigator-initiated pilot studies at MD Anderson Cancer Center were ongoing simultaneously, though the chosen regulatory approval path by the company was initially in lymphomas. Fortunately, by the time the phase III pivotal trial in lymphoma had failed to meet the primary study endpoint in 2014 [ 8 , 9 ], the pilot studies in ALL had shown encouraging results, thus shifting the regulatory focus to ALL.

The investigator-initiated phase II study at MD Anderson Cancer Center evaluated INO in children and adults with CD22-positive relapsed or refractory ALL (Table  1 ). In the initial publication, 49 patients received INO at a dose of 1.3 mg/m 2 to 1.8 mg/m 2 administered once every 3–4 weeks [ 10 ]. The population was heavily pretreated, with 73% of patients being treated as second or later salvage. The complete remission (CR)/CR with incomplete hematologic recovery (CRi) rate was 57%, and the median OS was 5.1 months. The most common adverse events were fever (59%), transaminase elevation (57%), and hyperbilirubinemia (29%). An important observation was that allogeneic hematopoietic stem cell transplantation (HSCT) increased the risk of toxicity. Among the 26 patients who underwent HSCT following INO, the 1-year OS rate was only 20%, driven by higher rates of non-relapse mortality (NRM) and 5 deaths due to sinusoidal obstruction syndrome (SOS) / veno-occlusive disease (VOD). To improve upon the safety/efficacy profile of INO, the study was then amended to fractionate the dose of INO and administer a dose of 0.8 mg/m 2 on day 1 and 0.5 mg/m 2 on day 8 and 15, given every 3–4 weeks, with the rationale that lower dose and more frequent schedules of INO may improve anti-ALL efficacy (which is determined primarily by the area under the curve) while reducing toxicities (which is determined primarily by the peak level of INO). In a subsequent analysis after treating 90 total patients (49 at the original schedule and 41 at the new schedule), the response rates and survival outcomes were similar [ 11 ]. However, the new dosing schedule appeared safer and resulted in lower rates of fever, hypotension and hyperbilirubinemia. The rate of SOS/VOD was also lower with the new schedule (7% versus 17% with the previous schedule), which may have been driven by the fractionated dosing as well as better understanding of the SOS/VOD risk with INO, leading to a reduced use of alkylating agents in HSCT preparative regimens.

The safety and efficacy of INO was later confirmed with a phase I/II multicenter study that evaluated INO in a similar population of adults with relapsed or refractory ALL (Table  1 ) [ 12 ]. This study also evaluated divided, weekly doses of INO (ranging from 1.2 mg/m 2 to 1.8 mg/m 2 per cycle) given for up to 6 cycles. The recommended phase II dose was 1.8 mg/m 2 per cycle, with the dose reduced to 1.6 mg/m 2 once CR/CRi was achieved. Seventy-two patients were treated, including 78% in salvage 2 or beyond and approximately one-third who had undergone previous allogeneic HSCT. The CR/CRi rate was 68% (including CR in 32%), and the median OS was 7.4 months. One-third of patients received a subsequent allogeneic HSCT, and there were 4 cases of SOS/VOD (6% total).

Phase III study (INO-VATE)

Efficacy and safety outcomes.

Based on the promising safety and efficacy data from the 2 prior clinical studies of INO in B-cell ALL, the INO-VATE study was designed as pivotal trial to compare INO to conventional chemotherapy in adults with relapsed or refractory CD22-positive B-cell ALL (Table  1 ) [ 1 ]. Three hundred and twenty-six patients were randomized 1:1 to INO or combination chemotherapy (either fludarabine, cytarabine and granulocyte-stimulating factor [FLAG], cytarabine plus mitoxantrone, or high-dose cytarabine). Given the superior safety observed with weekly dosing, INO was given at a dose of 0.8 mg/m 2 on day 1 and 0.5 mg/m 2 on days 8 and 15, for up to 6 cycles. The median age was 47 years in both arms, and 32% of patients in the INO arm and 36% in the control arm were in second salvage. INO resulted in a significantly higher rate of CR/CRi than did conventional chemotherapy (80.7% [95% confidence interval (CI), 72.1–87.7%] vs. 29.4% [95% CI, 21.0–38.8%], respectively; P  < 0.001). Superior responses with INO were observed across all subgroups, with the exception of patients with t(4;11), although the number of patients was small. Among responders, INO was also associated with significantly higher rates of MRD negativity by multiparameter flow cytometry (78.4% vs. 28.1%, respectively; P  < 0.001) and higher rates of subsequent HSCT (41% vs. 11%, respectively; P  < 0.001). Driven by the higher rates of response and HSCT realization, INO resulted in significantly better median OS (7.7 months [95% CI, 6.0 to 9.2] vs. 6.7 months [95% CI, 4.9 to 8.3]; P  = 0.04). While the numerical improvement in median OS was marginal, the greatest benefit to INO was observed in the long-term survival outcomes, where INO more than doubled the 2-year OS rate compared with chemotherapy (23% vs. 10%, respectively). Febrile neutropenia and thrombocytopenia were more common in the control group, while liver-related adverse events were more common with INO. The SOS/VOD rate with INO and chemotherapy were 11% and 1%, respectively. Based on the substantial improvement in both response rates and OS, the FDA approved INO in August 2017 for the treatment of adults with relapsed/refractory B-cell ALL.

Subgroups analyses, including transplant outcomes

Following the initial publication of the INO-VATE study, several subgroup analyses of the trial population have been published. These analyses have highlighted important considerations for the use of INO, including its good activity irrespective of bone marrow blast percentage, extramedullary involvement, or CD22 expression, and its activity in Philadelphia chromosome (Ph)-positive ALL [ 13 , 14 , 15 ]. INO is associated with a higher rate of HSCT realization, which is the most significant predictor of OS following INO therapy by multivariate analysis [ 16 ]. Among patients in the INO-VATE study who received INO and achieved CR/CRi, those who underwent subsequent allogeneic had the best outcomes (median OS 12.6 months and 2-year OS rate 39% versus median OS 7.1 months and 2-year OS rate 13% in non-transplanted). However, subsequent transplant is associated with higher risk of SOS/VOD after INO (23% versus 9% in non-transplanted patients), which contributes to INO-related non-relapse mortality. Proper patient selection for INO and mitigation strategies are therefore imperative to prevent this important potential copmlication. Similar post-transplant findings were observed in a pooled analysis of 2 INO studies, where patients who underwent allogeneic HSCT following INO had a post-HSCT median OS of 9.2 months and 2-year post-HSCT OS rate of 41% [ 17 ]. The overall rate of SOS/VOD among transplanted patients across these 2 studies was 18%.

Pooled analyses from multiple INO studies have been used to better understand the risk for SOS/VOD, which is a severe and potential toxicity with INO treatment. Across these studies, the predictors for the development of SOS/VOD include: older age, the use of double alkylator preparative regimens for HSCT, elevated pretreatment transaminases and/or bilirubin, more cycles and higher cumulative doses of INO, and multiple prior ALL therapies, especially prior HSCT [ 1 , 18 , 19 , 20 ]. Subsequent consensus guidelines have been developed to mitigate these risks. Important considerations to prevent the risk of SOS/VOD in patients receiving INO include: proper selection of patients (e.g. avoiding in patients were severe underlying hepatic dysfunction, avoiding dual alkylator conditioning regimens in transplanted patients, limiting INO to a cumulative dose of 2.7 to 3.6 mg/m 2 in patients proceeding to allogeneic HSCT, use of high dose steroids at the first sign of liver dysfunction, and distancing the last dose of INO from time of HSCT [ 21 ]. Ursodiol prophylaxis 300 mg three times daily should be considered for all patients receiving INO, although there is no clear role for defibrotide as prophylaxis, even for high-risk patients [ 22 ].

Combination therapies with INO for relapsed/refractory ALL

While single-agent INO therapy represents a therapeutic advance for patients with relapsed/refractory ALL, it is not curative for most patients when given as monotherapy, with < 20% of patients achieving long-term survival [ 16 ]. Research efforts have therefore been focused on combination therapies of INO with chemotherapy and/or other novel agents such as blinatumomab, with the goal of deepening response and further improving survival outcomes (Table  2 ). At MD Anderson Cancer Center, a regimen of mini-hyper-CVD (dose-reduced hyperfractionated cyclophosphamide, vincristine and dexamethasone alternating with dose-reduced methotrexate and cytarabine) in combination with INO was studied in relapsed/refractory Ph-negative B-cell ALL. Figure  2 shows the evolution of this regimen over the past decade. INO was originally given on day 3 of cycles 1–4 at a dose of 1.8 mg/m 2 in cycle 1 and 1.3 mg/m 2 in cycles 2–4 (cumulative dose of 5.7 mg/m 2 ) and then was later reduced to 1.3 mg/m 2 in cycle 1 and 1 mg/m 2 in cycles 2–4 (cumulative dose of 4.3 mg/m 2 ) in an effort to reduce the risk of SOS/VOD (Fig.  2 A) [ 23 ]. Among 59 patients treated, the overall response rate was 78%, with 82% of responders achieving MRD negativity by flow cytometry. Response rates were particularly encouraging in first salvage, where the overall response rate was 91%. The SOS/VOD rate was 15% using this single-dose regimen, which was similar to the 17% rate observed in the initial phase II study using a similar dosing strategy [ 10 ]. The median OS was 11 months, and the 1-year OS rate was 46%. The survival outcomes were compared to historical data with INO monotherapy using an inverse probability of treatment weighing analysis, which suggested that the combination therapy was superior to expectations with INO monotherapy.

Evolution of the hyper-CVD and inotuzumab ozogamicin ± blinatumomab regimen at MD Anderson Cancer Center. A .) Hyper-CVD plus inotuzumab ozogamicin, B .) Hyper-CVD plus inotuzumab ozogamicin with sequential blinatumomab, C .) “Dose dense” hyper-CVD, inotuzumab ozogamicin and blinatumomab

This study was then amended to further reduce and fractionate the dose of INO, add blinatumomab, and mandate ursodiol prophylaxis (Fig.  2 B) [ 24 ]. The purpose of these changes was two-fold: to deepen response with the addition of blinatumomab and to mitigate the risk of SOS/VOD by reducing the dose of INO and by increasing the interval between the last dose of INO and allogeneic HSCT. In this new design, patients received 4 cycles of mini-hyper-CVD plus INO, followed by 4 cycles of blinatumomab, and then a maintenance phase of blocks of POMP (6-mercaptopurine, vincristine, methotrexate, and prednisone) alternating with blinatumomab. INO was reduced to 0.6 mg/m 2 on day 2 and 0.3 mg/m 2 on day 8 in cycle 1 and 0.3 mg/m 2 on days 2 and 8 in cycles 2–4 (cumulative dose of 2.7 mg/m 2 ). In the most recent published analysis of the mini-hyper-CVD, INO ± blinatumomab regimen (with blinatumomab given to patients #68+), 110 patients have been treated [ 25 ]. The overall response rate was 83%, and 82% of responders achieved MRD negativity by flow cytometry. The median OS was 17 months, and the 3-year OS rate was 40%. Outcomes were best for those treated in first salvage, where the median OS was 31 months, and the 3-year OS rate was 49%. In a landmark analysis, there was no benefit for receipt of subsequent allogeneic HSCT (3-year OS 54% for both groups). The SOS/VOD rate was also observed to be lower after the amendment to reduce and fractionate INO and add blinatumomab (2% vs. 13% with the previous design; P  = 0.05). These data highlight that SOS/VOD can be substantially mitigated with use of lower doses of INO without compromising efficacy.

The mini-hyper-CVD, INO and blinatumomab regimen has now been amended to administer to deliver all agents beginning in cycle 1 (Fig.  2 C). In the latest study design, 6 cycles of “dose-dense” mini-hyper-CVD, INO and blinatumomab are given, followed by POMP/blinatumomab maintenance in non-transplanted patients. In each cycle, blinatumomab is started on day 4 (i.e. once the mini-hyper-CVD chemotherapy has been delivered) and continues through day 21 of each cycle, followed by a 7-day break before beginning the next cycle. To date, 15 patients with relapsed/refractory ALL have been treated with this regimen. All patients responded, with 92% achieving flow MRD negativity (77% after 1 cycle) [ 26 ]. High rates of early response have also been observed in a retrospective analysis of this regimen in both newly diagnosed and relapsed/refractory patients [ 27 ]. Among patients with newly diagnosed or MRD-positive ALL, 10/11 (91%) achieved MRD negativity at a level of 10 − 6 by next-generation sequencing, an endpoint shown to be associated with superior outcomes in ALL [ 28 , 29 ]. The deep and rapid MRD negative responses with the dose-dose mini-hyper-CVD, INO and blinatumomab regimen are encouraging, and this regimen is also now being evaluated in older adults with newly diagnosed B-cell ALL.

Combination therapies with INO for newly diagnosed ALL

Older adults.

Several studies are also evaluating INO in patients with newly diagnosed ALL. Most of these efforts have focused on its use in older adults, a group with poor tolerance to conventional chemotherapy and with historical long-term OS rates of only 20% [ 31 , 32 ]. Ongoing trials exploring INO in the frontline setting are shown in Table  3 , and a summary of available trial data of INO-based regimens in older adults with ALL is shown in Table  4 . At MD Anderson Cancer Center, the same mini-hyper-CVD plus INO regimen previously described was also studied in patients ≥ 60 years of age with newly diagnosed Ph-negative B-cell ALL [ 32 ]. Initially, 52 patients with a median age of 68 years were treated. The overall response rate was 98%, with 96% of patients achieving MRD negativity by flow cytometry. These high rates of response translated to encouraging long-term survival with 3-year progression-free survival (PFS) and OS rates of 49% and 56%, respectively. As with the relapsed/refractory study, this regimen was later amended to use lower, fractionated doses of INO (cumulative dose 2.7 mg/m 2 ), add blinatumomab and mandate ursodiol prophylaxis. A total of 80 older patients have been treated with the mini-hyper-CVD, INO ± blinatumomab regimen (patients #50 + treated with the updated regimen) [ 33 ]. Twelve patients (15%) have relapsed, and the 5-year PFS and OS rates are 44% and 46%, respectively. These outcomes compare favorably to the historical 5-year OS rate of approximately 20% when chemotherapy alone is used. The superiority of the mini-hyper-CVD, INO and blinatumomab regimen as compared with dose-reduced hyper-CVAD in a similar older population was confirmed in a propensity score analysis [ 34 ].

Despite the improvement over historical expectations, toxicity is still a significant concern with this regimen. Overall, 35 patients (44%) died in remission (including 9 from myelodysplastic syndrome or acute myeloid leukemia, 8 from infection and 5 from SOS/VOD). The risk of death in remission was higher in patients ≥ 70 years of age (accounting for 85% of deaths in remission), resulting in age-dependent survival outcomes (median OS 75 months, 47 months, and 35 months for patients 60–64, 65–69 and ≥ 70 years of age, respectively). Due to the specific risks related to the chemotherapy backbone (e.g. secondary myeloid malignancy and infection), patients ≥ 70 years of age will now receive INO and blinatumomab only, without the mini-hyper-CVD backbone. A similar approach has been evaluated in the Alliance A041703 study [ 35 ]. In this trial, patients ≥ 60 years of age with newly diagnosed Ph-negative B-cell ALL received induction with fractionated INO at 1.8 mg/m 2 in cycle 1 and 1.5 mg/m 2 in cycle 2, followed by consolidation with blinatumomab for 4–5 cycles. Among 33 patients treated, the overall response rate was 96% (85% after INO induction), and the 1-year OS rate was 84%. Longer term follow-up will be needed to confirm the durability of these responses.

Several other INO-based frontline regimens are being evaluated in older adults with newly diagnosed ALL. In the INITIAL-1 study, patients > 55 years of age with newly diagnosed Ph-negative B-cell ALL received induction with 3 cycles of dexamethasone plus INO (1.8 mg/m 2 in cycle 1 and 1.5 mg/m 2 in cycles 2–3), followed by 6 cycles of age-adjusted chemotherapy as consolidation/maintenance. 37 Forty-three patients were treated with a median age of 64 years (range, 56–80 years). All patients achieved CR/CRi, with 71% achieving MRD negativity at a sensitivity of 10 − 4 after the 3 cycles of INO induction. The 3-year event-free survival (EFS) and OS rates were 55% and 73%, respectively, and there was only 1 case of non-fatal SOS/VOD. The EWALL-INO study also enrolled a similar population of patients and treated them with 2 cycles of induction consisting of INO, vincristine and dexamethasone (induction 1) and INO, cyclophosphamide and dexamethasone (induction 2), followed by 6 cycles of age-adjusted consolidation and then POMP maintenance [ 37 ]. Overall, 131 patients were treated, and the CR/CRi rate after 2 cycles of induction was 90%. The estimated 2-year OS rate was 54%. Taken together, these studies show that frontline INO-based therapy is safe and effective in older adults with B-cell ALL. Building on the promising experience with the mini-hyper-CVD and INO regimen from MD Anderson, the Alliance A042001 is a randomized phase II study evaluating mini-hyper-CVD plus INO versus dose-adjusted hyper-CVAD in older adults (≥ 50 years of age) with newly diagnosed B-cell ALL [ 38 ]. No data are yet available, and this study is ongoing.

Younger adults

Combination approaches using INO are also being explored in younger adults with newly diagnosed ALL. At MD Anderson, we developed a protocol of hyper-CVAD plus blinatumomab, which has now been amended to add INO. The hyper-CVAD plus blinatumomab regimen consists of 4 cycles of hyper-CVAD, followed by 4 cycles of blinatumomab, and then POMP and blinatumomab maintenance. In the first 38 patients treated, all patients responded, with 97% becoming MRD negative by flow cytometry. This translated to encouraging 3-year relapse-free survival (RFS) and OS rates of 73% and 81%, respectively [ 39 ]. An additional 37 patients have now been treated with the addition of INO (0.3 mg/m 2 on day 1 and 8 of cycles 2, 4, 6 and 8; cumulative dose of 2.4 mg/m 2 ) [ 41 ]. With a median follow-up of 22 months, only 3 relapses have been observed. The estimated 2-year RFS and OS rates of 88% and 100%, respectively. The initial data with the addition of INO are encouraging and suggest a potential benefit with the routine use of INO in younger patients with newly diagnosed Ph-negative B-cell ALL.

Of note, the Alliance A041501 was a randomized study that also evaluated the addition of INO to standard chemotherapy (CALGB 10,403 backbone) in newly diagnosed B-cell ALL. This study was suspended due to toxicity concerns with the combination regimen, possibly related to the use of multiple hepatoxic agents in this regimen (e.g. INO and asparaginase). The lack of success of this study highlights the need for rationale combinations with INO and to avoid overlapping toxicities.

Other investigational applications of INO in ALL

Ino for mrd-positive disease.

In the INO-VATE study, INO was associated with a flow MRD negativity rate of 63% among responders [ 41 ] and provided support for the evaluation of INO for MRD-positive B-cell ALL. In a phase II study, 26 patients with MRD-positive ALL were enrolled and treated with INO at a dose of 0.6 mg/m 2 and 0.3 mg/m 2 on days 1 and 8, respectively, of cycle 1 and 0.3 mg/m 2 on day 1 and 8 of cycles 2-6 [ 42 ]. Sixteen patients (62%) had Ph-positive ALL and also received a BCR::ABL1 TKI (predominantly ponatinib). The MRD negativity response at a sensitivity of 10 − 4 was 69%, which translated to a 2-year OS rate of 60%. In another study from GIMEMA, INO was evaluated in 20 patients with MRD-positive B-cell ALL. Eleven of 20 patients (55%) achieved MRD response < 10 − 4 [ 44 ]. These encouraging data support the further of evaluation of INO as an MRD-directed therapy in ALL and also provide support for its continued evaluation in the frontline setting to induce deep, MRD-negative remissions.

INO for Ph + ALL

INO is active in relapsed/refractory Ph-positive ALL and achieves a CR/CRi rate of 73% and median OS of 8.7 months, which are similar to the findings from the broader population of the INO-VATE study [ 14 ]. In a phase I/II study, INO was combined with bosutinib in patients with relapsed/refractory Ph-positive ALL who did not harbor a T315I mutation [ 44 ]. Among 18 patients (16 with Ph-positive ALL and 2 with CML in lymphoid blast phase), the CR/CRi rate was 83%, with 56% achieving a complete molecular response. The median OS was 13.5 months, which appears superior to expectations with INO as monotherapy.

INO as post-transplant maintenance

INO has been evaluated as post-transplant maintenance in a phase I study of patients with CD22-positive ALL and high-risk for relapse [ 45 ]. INO doses of 0.3 mg/m 2 to 0.6 mg/m 2 were administered once per cycle for up to 12 cycles. The MTD was 0.6 mg/m 2 . Among 18 treated patients, no cases of SOS/VOD were observed. With a median follow-up of 18.1 months, only 2 relapses were observed, and the 1-year PFS and OS rates were 89% and 94%, respectively. This study suggests that low-dose INO can be safely administered in the peri-transplant setting and may also be helpful in preventing relapse in high-risk patients.

Sequencing of INO with CAR T-cell therapy

In clinical practice, INO is commonly given prior to CAR T-cell therapy, either as a salvage regimen and as bridging therapy. However, the data are mixed regarding whether prior INO exposure impacts the effectiveness of CAR T-cells [ 46 , 47 , 48 ]. Some studies in children have suggested that prior INO—including INO as bridging therapy—did not impact response rates or long-term outcomes following tisagenlecleucel, as compared with historical expectations [ 46 , 47 ]. However, in the ZUMA-3 study of brexucabtagene autoleucel in adult patients, those with prior INO exposure had numerically lower CR/CRi rates (59% with prior INO exposure versus 77% without prior INO exposure) and inferior OS (median OS 8.8 months and 47.0 months, respectively) [ 48 ]. Future studies evaluating the optimal sequencing of INO with other available therapies—including blinatumomab and CD19 CAR T-cells—and the use of INO as bridging therapy prior to CAR T-cell therapy are needed.

Conclusions

Along with the blinatumomab and CAR T-cells, the clinical development of INO has been a major contributor to improving outcomes of adult ALL over the past decade [ 49 ]. While INO has been shown to be more effective than conventional cytotoxic chemotherapy in relapsed/refractory B-cell ALL, its greatest potential is as combination therapy in both the frontline and salvage settings. When used along with low-dose chemotherapy and blinatumomab in relapsed/refractory ALL, a 3-year OS rate > 50% has been observed, even in non-transplanted patients. Similarly, very encouraging outcomes have been observed with INO in newly diagnosed B-cell ALL, whether combined with chemotherapy, blinatumomab or both. Over the course of these studies, the INO dose has been modified, with some studies suggesting that lower, fractionated doses of INO can be highly effective and may also reduce the risk of SOS/VOD, which is one of the feared potential toxicities of INO. Studies continue to expand the potential applications of INO, including its use for MRD-positive disease, combination with BCR::ABL1 tyrosine kinase inhibitors, and its use in low doses as post-transplant maintenance. Many of these ongoing research efforts seek to explore alternative dosing strategies of INO. New translational research is also seeking to understand the mechanisms of resistance to INO, which may help to inform future rational drug combinations [ 50 , 51 , 52 , 53 ]. The FDA approval of INO in 2017 marked a major milestone that paved the way for these important studies, but it is imperative to note that this was just one step in the clinical development of INO. The research that has followed in the years since the INO-VATE study highlight a truism in oncology: that regulatory approval of a drug is often only the beginning of its true clinical development and innovation.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Acute lymphoblastic leukemia

Confidence interval

Chronic myeloid leukemia

Complete remission

Complete remission with incomplete hematologic recovery

Event-free survival

Food and Drug Administration

Hematopoietic stem cell transplantation

Inotuzumab ozogamicin

Dose-attenuated hyperfractionated cyclophosphamide, vincristine and dexamethasone alternating with methotrexate and cytarabine

Measurable residual disease

Maximum tolerated dose

Non-relapse mortality

Overall survival

Progression-free survival

Philadelphia chromosome

6-mercaptopurine, vincristine, methotrexate, and prednisone

Relapse-free survival

Sinusoidal obstruction syndrome

Tyrosine kinase inhibitor

Veno-occlusive disease

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Acknowledgements

The authors wish to thank Lewis Nasr MD and Omer Karrar MD for their assistance with creation of tables and formatting of the manuscript.

This research is supported in part by the MD Anderson Cancer Center Leukemia SPORE CA100632, and the NIH/NCI Cancer Center Support Grant P30 CA016672.

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N.J.S. has served as consultant for Pfizer Inc., GSK, NKARTA, Autolus, and Sanofi, reports receiving research grants from Takeda Oncology, Astellas Pharma Inc., Xencor, Stemline Therapeutics, and NextCure, and has received honoraria from Adaptive Biotechnologies, Novartis, Amgen, Takeda Oncology, Pfizer Inc., Astellas Pharma Inc., Sanofi and BeiGene. E.J. reports receiving research grants and consultancy fees from AbbVie, Adaptive Biotechnologies, Amgen, Ascentage, Bristol Myers Squibb, Genentech, Incyte, Pfizer, and Takeda. N.J. reports receiving research grants from Pharmacyclics, AbbVie, Genentech, AstraZeneca, BMS, Pfizer, ADC Therapeutics, Cellectis, Adaptive Biotechnologies, Precision Biosciences, Fate Therapeutics, Kite/Gilead, Mingsight, Takeda, Medisix, Loxo Oncology, Novalgen, Dialectic Therapeutics, Newave, TransThera Sciences, Novartis, Carna Biosciences, Sana Biotechnology, Kisoji Biotechnology, and has received honoraria from Pharmacyclics, Janssen, AbbVie, Genentech, AstraZeneca, BMS, Adaptive Biotechnologies, Kite/Gilead, Precision Biosciences, Beigene, Cellectis, MEI Pharma, Ipsen, CareDX, MingSight, and Novalgen. H.K. reports receiving research grants from AbbVie, Agios, Amgen, Ariad, Astex, BMS, Cyclacel, Daiichi-Sankyo, Immunogen, Jazz Pharma, Novartis, Pfizer, Actinium, and Takeda.

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Short, N.J., Jabbour, E., Jain, N. et al. Inotuzumab ozogamicin for the treatment of adult acute lymphoblastic leukemia: past progress, current research and future directions. J Hematol Oncol 17 , 32 (2024). https://doi.org/10.1186/s13045-024-01552-7

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A chemical linked to a higher risk of leukemia and other blood cell cancers creeps into millions of homes whenever residents light their gas stoves. A new Stanford-led analysis finds that a single gas cooktop burner on high or a gas oven set to 350 degrees Fahrenheit can raise indoor levels of the carcinogen benzene above those in secondhand tobacco smoke. Benzene also drifts throughout a home and lingers for hours in home air, according to the paper published June 15 in Environmental Science & Technology .

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A Stanford-led analysis finds that a single gas cooktop burner on high or a gas oven set to 350 F can raise indoor levels of the carcinogen benzene above those in secondhand tobacco smoke.

“Benzene forms in flames and other high-temperature environments, such as the flares found in oil fields and refineries. We now know that benzene also forms in the flames of gas stoves in our homes,” said study senior author Rob Jackson , the Michelle and Kevin Douglas Provostial Professor and professor of Earth system science at the Stanford Doerr School of Sustainability . “Good ventilation helps reduce pollutant concentrations, but we found that exhaust fans were often ineffective at eliminating benzene exposure.”

Worse than secondhand smoke

Overall, the researchers found that indoor concentrations of benzene formed in the flames of gas stoves can be worse than average concentrations from secondhand smoke, that benzene can migrate into other rooms far from the kitchen, and that concentrations measured in bedrooms can exceed national and international health benchmarks. They also found residential range hoods are not always effective at reducing concentrations of benzene and other pollutants, even when the hoods vent outdoors.

The new paper is the first to analyze benzene emissions when a stove or oven is in use. Previous studies focused on leaks from stoves when they are off, and did not directly measure resulting benzene concentrations. The researchers found gas and propane burners and ovens emitted 10 to 50 times more benzene than electric stoves. Induction cooktops emitted no detectable benzene whatsoever. The rates of benzene emitted during combustion were hundreds of times higher than benzene emission rates identified in other recent studies from unburned gas leaking into homes.

The researchers also tested whether foods being cooked emit benzene and found zero benzene emissions from pan-frying salmon or bacon. All benzene emissions the investigators measured came from the fuel used rather than any food cooked.

A previous Stanford-led study showed that gas-burning stoves inside U.S. homes leak methane with a climate impact comparable to the carbon dioxide emissions from about 500,000 gasoline-powered cars. They also expose users to pollutants, such as nitrogen dioxide, which can trigger respiratory diseases. A 2013 meta-analysis concluded that children who live in homes with gas stoves had a 42% greater risk of asthma than children living in homes without gas stoves, and a 2022 analysis calculated that 12.7% of childhood asthma in the U.S. is attributable to gas stoves .

“I’m renting an apartment that happens to have an electric stove,” said study lead Yannai Kashtan , a graduate student in Earth system science. “Before starting this research, I never thought about it twice, but the more we learn about pollution from gas stoves, the more relieved I am to be living without a gas stove.”

Jackson is also a senior fellow at the Stanford Woods Institute for the Environment and the Precourt Institute for Energy . Study co-authors also include Metta Nicholson and Colin Finnegan , environmental science research professionals in Stanford’s Earth System Science Department; Zutao Ouyang , a physical science research associate in Stanford’s Earth System Science Department; and researchers at PSE Healthy Energy, the University of California, Berkeley, and Lawrence Berkeley National Lab.

The study was funded by the High Tide Foundation.

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The HPV vaccine prevents head and neck cancers in men, study suggests

The HPV vaccine is linked to a drastic reduction in head and neck cancers in adolescent boys and men, new research finds. 

HPV, or human papillomavirus, is a sexually transmitted infection responsible for virtually all cases of cervical cancer . But the virus is also linked to a number of other cancers , including penile, anal and vaginal cancers. 

It also accounts for the majority — up to 70% — of head and neck cancers , which affect the throat and mouth. Men are about twice as likely to develop these cancers than women, according to the National Cancer Institute.

The HPV vaccine, initially approved for adolescent girls, protects against strains of the virus linked to cervical cancer and has been found to significantly reduce rates of the cancer . But there’s growing evidence that the vaccine also protects against other HPV-related cancers.

“We want males to be thinking about HPV vaccination not just as something that protects female patients, but also male patients,” said Jefferson DeKloe, a research fellow in the department of otolaryngology at Thomas Jefferson University, who specializes in head and neck surgery and who co-authored the research.

The findings will be presented next week at the American Society of Clinical Oncology conference and have not yet been published in a peer-reviewed journal. 

Prior research showed a downward trend in oral infections with HPV strains known to cause cancer. That was a promising sign, said Dr. Glenn J. Hanna, a medical oncologist at the Dana-Farber Cancer Institute’s Center for Head and Neck Oncology, who was not involved in the new research.

“If we can lower the infection rate, we would hope that we would see what we are seeing now, a decline in cancer rates,” Hanna said. “This is an important evolution of the story.” 

The new study analyzed health records from a national database that included nearly 3.5 million people in the United States ages 9 to 39 who had received any vaccination — HPV or otherwise — from 2010 through 2023. About 1.5 million were male, half of whom had been vaccinated against cancer-causing strains of HPV. Nearly 1 million were females who had been vaccinated against HPV. 

The researchers compared the rates of HPV-linked cancers — including head and neck, anal, penile, and cervical cancers — in people who had received the HPV vaccine to those who hadn’t. They found being vaccinated reduced the overall risk of HPV-related cancers in males by 54%, a decrease driven primarily by a drop in head and neck cancers. Females were about 30% less likely to develop any type of HPV-related cancer, including cervical cancer. 

Most cases of head and neck cancer are in people older than 50. Since the U.S. is only about a decade into widespread HPV vaccination in both males and females, the vaccinated generation hasn’t reached this age yet. HPV typically infects younger people and takes decades for chronic infection to lead to cancer.

“These are the early results of a larger phenomenon we are going to watch play out over the next 20 or 30 years,” DeKloe said, noting that experts don’t expect to see the full effect HPV vaccination has on cancer rates until the largely vaccinated generation is older. 

A second study, which will also be presented at the ASCO conference next week and is not yet published in a peer-reviewed journal, found that HPV vaccination rates have been on the rise in the U.S. from 2011 through early 2020, including in all racial and ethnic groups.

HPV vaccination wasn’t recommended for males until 2011, five years after the Centers for Disease Control and Prevention recommended the vaccine series for girls. The HPV vaccine is now recommended for all adolescents starting as young as age 9, but can also be given to adults up to age 45. 

In the new study, which included children and young adults ages 9 to 26, the increase was largely driven by growing HPV vaccine uptake among males. Although overall HPV vaccination rates among males still lag behind females — about 36% compared to about 50% of those in the 9 to 26 age group — these rates are accelerating. 

“The gap is narrowing between males and females and eventually I would hope that they would meet up,” said Dr. Danh Nguyen, a resident physician at University of Texas Southwestern Medical Center, who led the research. 

Although vaccination efforts have focused on adolescents, adults should also consider getting vaccinated if they weren’t when they were younger, said Dr. Nancy Lee, service chief of head and neck radiation oncology at Memorial Sloan Kettering Cancer Center in New York City, who was not involved with either study. 

“If you are in your 20s or 30s, you can still get the vaccination. Even if you are 45, there is no reason you cannot get vaccinated because we have a population that lives a long time,” Lee said. 

Nguyen said it’s important that conversations about HPV vaccination continue to focus on the prevention of all cancers, including head and neck cancers that are more prevalent in men, rather than solely on cervical cancer prevention.

Hanna said stigma around HPV being a sexually transmitted infection has made discussions around vaccinating adolescents a sticky subject in the past, but that clear data showing the impact vaccination rates have on HPV-related cancers is shifting the narrative. 

“HPV vaccination is cancer prevention,” Hanna said. “The bottom line is that we are preventing cancers broadly by getting people vaccinated younger.”

Kaitlin Sullivan is a contributor for NBCNews.com who has worked with NBC News Investigations. She reports on health, science and the environment and is a graduate of the Craig Newmark Graduate School of Journalism at City University of New York.

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Study explains why the brain can robustly recognize images, even without color

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Even though the human visual system has sophisticated machinery for processing color, the brain has no problem recognizing objects in black-and-white images. A new study from MIT offers a possible explanation for how the brain comes to be so adept at identifying both color and color-degraded images.

Using experimental data and computational modeling, the researchers found evidence suggesting the roots of this ability may lie in development. Early in life, when newborns receive strongly limited color information, the brain is forced to learn to distinguish objects based on their luminance, or intensity of light they emit, rather than their color. Later in life, when the retina and cortex are better equipped to process colors, the brain incorporates color information as well but also maintains its previously acquired ability to recognize images without critical reliance on color cues.

The findings are consistent with previous work showing that initially degraded visual and auditory input can actually be beneficial to the early development of perceptual systems.

“This general idea, that there is something important about the initial limitations that we have in our perceptual system, transcends color vision and visual acuity. Some of the work that our lab has done in the context of audition also suggests that there’s something important about placing limits on the richness of information that the neonatal system is initially exposed to,” says Pawan Sinha, a professor of brain and cognitive sciences at MIT and the senior author of the study.

The findings also help to explain why children who are born blind but have their vision restored later in life, through the removal of congenital cataracts, have much more difficulty identifying objects presented in black and white. Those children, who receive rich color input as soon as their sight is restored, may develop an overreliance on color that makes them much less resilient to changes or removal of color information.

MIT postdocs Marin Vogelsang and Lukas Vogelsang, and Project Prakash research scientist Priti Gupta, are the lead authors of the study, which appears today in Science . Sidney Diamond, a retired neurologist who is now an MIT research affiliate, and additional members of the Project Prakash team are also authors of the paper.

Seeing in black and white

The researchers’ exploration of how early experience with color affects later object recognition grew out of a simple observation from a study of children who had their sight restored after being born with congenital cataracts. In 2005, Sinha launched Project Prakash (the Sanskrit word for “light”), an effort in India to identify and treat children with reversible forms of vision loss.

Many of those children suffer from blindness due to dense bilateral cataracts. This condition often goes untreated in India, which has the world’s largest population of blind children, estimated between 200,000 and 700,000.

Children who receive treatment through Project Prakash may also participate in studies of their visual development, many of which have helped scientists learn more about how the brain's organization changes following restoration of sight, how the brain estimates brightness, and other phenomena related to vision.

In this study, Sinha and his colleagues gave children a simple test of object recognition, presenting both color and black-and-white images. For children born with normal sight, converting color images to grayscale had no effect at all on their ability to recognize the depicted object. However, when children who underwent cataract removal were presented with black-and-white images, their performance dropped significantly.

This led the researchers to hypothesize that the nature of visual inputs children are exposed to early in life may play a crucial role in shaping resilience to color changes and the ability to identify objects presented in black-and-white images. In normally sighted newborns, retinal cone cells are not well-developed at birth, resulting in babies having poor visual acuity and poor color vision. Over the first years of life, their vision improves markedly as the cone system develops.

Because the immature visual system receives significantly reduced color information, the researchers hypothesized that during this time, the baby brain is forced to gain proficiency at recognizing images with reduced color cues. Additionally, they proposed, children who are born with cataracts and have them removed later may learn to rely too much on color cues when identifying objects, because, as they experimentally demonstrated in the paper, with mature retinas, they commence their post-operative journeys with good color vision.

To rigorously test that hypothesis, the researchers used a standard convolutional neural network, AlexNet, as a computational model of vision. They trained the network to recognize objects, giving it different types of input during training. As part of one training regimen, they initially showed the model grayscale images only, then introduced color images later on. This roughly mimics the developmental progression of chromatic enrichment as babies’ eyesight matures over the first years of life.

Another training regimen comprised only color images. This approximates the experience of the Project Prakash children, because they can process full color information as soon as their cataracts are removed.

The researchers found that the developmentally inspired model could accurately recognize objects in either type of image and was also resilient to other color manipulations. However, the Prakash-proxy model trained only on color images did not show good generalization to grayscale or hue-manipulated images.

“What happens is that this Prakash-like model is very good with colored images, but it’s very poor with anything else. When not starting out with initially color-degraded training, these models just don’t generalize, perhaps because of their over-reliance on specific color cues,” Lukas Vogelsang says.

The robust generalization of the developmentally inspired model is not merely a consequence of it having been trained on both color and grayscale images; the temporal ordering of these images makes a big difference. Another object-recognition model that was trained on color images first, followed by grayscale images, did not do as well at identifying black-and-white objects.

“It’s not just the steps of the developmental choreography that are important, but also the order in which they are played out,” Sinha says.

The advantages of limited sensory input

By analyzing the internal organization of the models, the researchers found that those that begin with grayscale inputs learn to rely on luminance to identify objects. Once they begin receiving color input, they don’t change their approach very much, since they’ve already learned a strategy that works well. Models that began with color images did shift their approach once grayscale images were introduced, but could not shift enough to make them as accurate as the models that were given grayscale images first.

A similar phenomenon may occur in the human brain, which has more plasticity early in life, and can easily learn to identify objects based on their luminance alone. Early in life, the paucity of color information may in fact be beneficial to the developing brain, as it learns to identify objects based on sparse information.

“As a newborn, the normally sighted child is deprived, in a certain sense, of color vision. And that turns out to be an advantage,” Diamond says.

Researchers in Sinha’s lab have observed that limitations in early sensory input can also benefit other aspects of vision, as well as the auditory system. In 2022, they used computational models to show that early exposure to only low-frequency sounds, similar to those that babies hear in the womb, improves performance on auditory tasks that require analyzing sounds over a longer period of time, such as recognizing emotions. They now plan to explore whether this phenomenon extends to other aspects of development, such as language acquisition.

The research was funded by the National Eye Institute of NIH and the Intelligence Advanced Research Projects Activity.

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Future Research Directions

• First Online: 01 January 2012

Cite this chapter

• Khe Foon Hew 3 &
• Wing Sum Cheung 3  

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In this chapter, we suggest several future research directions concerning the use of asynchronous online discussion in education contexts. These directions include the following possibilities: (a) examining the use of peer facilitation in different contexts such as fully online environments, (b) investigating the possible solutions to overcome the strategy dilemmas, and (c) studying the use of online discussion on mobile devices such as pocket PCs and smart phones. In the Epilogue, we formulate a strategy framework based on the major findings on peer-facilitated online discussion environments described earlier in this book. This framework consists of three critical stages: initialization, engagement, and closure. Taken together this strategy framework has the potential to attract participant online contribution, sustain the online discussions, and promote higher knowledge construction levels in peer-facilitated environments.

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Hew, K.F., Cheung, W.S. (2012). Future Research Directions. In: Student Participation in Online Discussions. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-2370-6_10

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How to Thrive as You Age

Like to bike your knees will thank you and you may live longer, too.

Allison Aubrey

A large new study shows people who bike have less knee pain and arthritis than those who do not. PamelaJoeMcFarlane/Getty Images hide caption

A large new study shows people who bike have less knee pain and arthritis than those who do not.

We are in the middle of National Bike Month , and cycling enthusiasts love to talk up the benefits of their favorite activity.

"It's definitely my longevity drug," says Brooks Boliek, 65, an avid cyclist of many decades, who used to commute to his office on a bicycle.

A substantial body of evidence supports the health benefits of cycling, everything from strengthening the immune system to boosting the likelihood of living longer. Now, a new study finds people who are in the habit of riding a bike are significantly less likely to have osteoarthritis and experience pain in their knees by age 65, compared to people who don't bike.

The study, which was funded in part by the National Institutes of Health, and published in the American College of Sports Medicine's flagship peer-reviewed journal, included about 2,600 men and women, with an average age of 64 years old. They were surveyed about their physical activity over their lifetime. As part of the study, researchers took X-ray images to evaluate signs of arthritis in their knee joints. "Bicyclers were 21% less likely to have X-ray evidence and symptoms of osteoarthritis compared to those who did not have a history of bicycling," explains study author Dr. Grace Lo of Baylor College of Medicine.

"I was surprised to see how very strong the benefit was," Lo says given the profile of the participants. The people enrolled in the study were not competitive athletes, but rather "average" people, ranging from their mid-40's up to 80 years old. All of them had elevated risks of developing knee arthritis due to weight, family history or former injuries.

The study can not prove cause and effect, given it was an observational study that assessed osteoarthritis at one point in time. But the findings, which are published in the journal Medicine & Science in Sports & Exercise , validate the advice many health care providers give to patients about the benefits of cycling and other non weight-bearing exercises.

"Cycling is very low impact," says musculoskeletal researcher Matt Harkey , an assistant professor at Michigan State University and a co-author of the study. Cycling also helps to build strength in the muscles around the knee which can help protect the joint. In addition, the rhythmic motion of pedaling on a bicycle can move synovial fluid , the viscous, egg white -like liquid in joints that helps reduce friction and absorb shock. "What it does is help to circulate the synovial fluid throughout the joint to help to kind of lubricate [the joint] and provide nutrient delivery to the cartilage," Harkey says.

Cycling enthusiast Brooks Boliek calls biking his "longevity drug," and the research backs him up on that. Allison Aubrey/NPR hide caption

Cycling enthusiast Brooks Boliek calls biking his "longevity drug," and the research backs him up on that.

Of course, there are many types of exercise that are good for health, though cycling seems to have a leg up when it comes to protecting joints. Oftentimes, people give up contact sports such as basketball, as they age, given the risk of injury.

"It can be expected that physical activity in which there is little weight-bearing on joints will be more beneficial than those that need constant stamping," such as running, says Norman Lazarus, a professor emeritus at King's College London, who is in his late 80's and is still cycling. (NPR profiled his cycling research in 2018.)

Lazarus says the results of the new study – pointing to a benefit – are not surprising, though he points out that biking does bring risk of injury . He says it's important for cyclists to understand the risk of overuse injuries as well as the importance of technique and getting a proper fitting bike. Each year, thousands of bicyclists are injured in motor vehicle crashes, and older adults are at higher risk of serious injury. Research shows it's safer to bike on trails or paths separated from traffic.

Risks, aside, research shows biking is good for longevity. "There's good data to support that people live longer when they bicycle," says Lo. She points to a study that found people who cycled one hour per week were about 22% less likely to die prematurely. This was a study of people with diabetes, so it's possible that the benefits are greater for people without the disease.

"This is an exercise [people] can participate in over a lifetime," Lo says, and it can also be done indoors on a stationary bike. "I think that it is a great preventative strategy for many things, including arthritis," she says.

Biking enthusiast Brooks Boliek says cycling brings him joy and a sense of accomplishment. "I'm very goal oriented," he says, and a daily ride gives him something to focus on. "It gives me something to live for."

A sense of purpose that keeps his heart pumping and his muscles strong. He says he'd love to keep riding until the day he dies.

Find Allison Aubrey on Instagram at @allison.aubrey and on X @AubreyNPR .