statement of purpose phd biostat

A Biostatistics PhD Application Notebook [with Statement of Purpose]

Katherine Hoffman

August 14, 2023

A minor life update – I applied to Biostatistics PhD programs last fall! And, a major life update– I’m moving to Seattle to attend the University of Washington (UW)’s program next month . I’m super excited (and nervous) to begin. Since applications are opening up for next year, I thought I’d share what the process of deciding to apply, actually applying, and deciding on a program was like for me.

Background and FAQs

I was in a somewhat uncommon situation when I applied in Fall 2022 for Fall 2023 matriculation to Biostatistics PhD programs. I received my MS in Biostatistics in 2018 and have been working in academic medical research since. Because of this, I had many academic biostatistician colleagues and friends to consult about my application. Since not everyone has this opportunity, I thought I’d pass on what was told to me, especially the advice I was given on writing my statement of purpose (SOP). I found the SOP advice so helpful that I’ve publicly shared mine in this Google Doc and added advice I received in the comments.

I am by no means an expert at what biostatistics graduate programs are looking for, and this is not a comprehensive post on applying to (biostatistics) graduate school. For more general and thorough information, I recommend Lucy Lai’s post on applying to graduate programs and Simon Couch’s post on applying to statistics and biostatistics graduate programs . Nonetheless, perhaps some of you might find my experience useful, or you might be able to pass it on to a future applicant who will find it useful!

Do you need a PhD to be successful in biostatistics?

A question I’m frequently asked from students and early-career biostatisticians is whether I feel a PhD is necessary because of a “ceiling” in biostatistics. My answer was and still is: a PhD is absolutely not necessary. In fact, for a while, I was resistant to the idea of pursuing one. It’s a huge financial and personal commitment, and it’s worth carefully examining whether it’s the right decision for you, now or ever.

I wholeheartedly believe I could have been fulfilled intellectually and financially in Masters-level statistician/data science jobs forever. There are many interesting roles in both industry and academic research. Some are heavily programming related, some are much more statistics-heavy, and other roles involve supervision/management as the primary form of career progression. A (bio)statistics degree is extremely versatile because you can pivot to areas such as data engineering, software writing, data reporting/querying/interpreting, and more. There have been multiple times over the past five years that I’ve seriously considered trying out data journalism and/or data visualization roles.

However, I really love studying and teaching causal inference and statistics. Beginning around Spring 2022, I had a nagging feeling that it might be the right time in my life to deeply learn many concepts I’ve struggled to learn for years without formal coursework and training. I wanted to learn these concepts not because I particularly want to develop new methods as an academic researcher, or to make lots of money in industry, but rather because I see huge gaps in the statistical and epidemiological methods which are available and what are actually being used in applied research. I know I will feel fulfilled the rest of my career if I can work on improving these gaps, either through education, scientific communication, or mentorship.

Should you take time off before starting your PhD?

The other questions I’ve been asked are (1) whether it was intentional to take “so much time” off and/or (2) whether I’m glad I waited to go back for my PhD. My answer to the first question is that, no, it wasn’t intentional, because five years ago I was not planning to get a PhD. The second question is somewhat difficult to answer before I start my program (it might be really hard to go back to school, I have no idea!), but at the moment, I am super happy I took a break between finishing the MS and starting the PhD. I had plenty of time to narrow and pursue my interests without any pressure and while making good money. I also had the chance to learn work-life balance, which I wasn’t great at until a few years ago. I was able to build up my application through an abundance of research and teaching opportunities, and this allowed me to be a far more competitive applicant than I would’ve been out of my MS. Finally, I learned a lot about myself! I feel so much more emotionally mature and resilient than I was five years ago.

The Application Process

I’ll try to be as honest as possible about my personal experience in applying. I hope none of the information I provide deters anyone from applying to any schools because they have a different background than me. I’ve been working full-time for five years, so I necessarily have a different background than someone applying during their undergraduate or Masters degree. However, PhD programs accept many, many students directly out of undergraduate and Masters programs, so my successes and failures should not be considered to be predictive of someone else’s.

Assessing which schools to apply to

I gauged my competitiveness for applying to PhD programs by asking biostatistics faculty I knew from working in the field and/or who taught me during my MS. I also searched Reddit for relevant content ( r/biostatistics ) and used Gradcafe . (Be wary of anonymous forums on the internet, though!)

From these sources, I gathered that my strengths were probably:

having a MS in Biostatistics already
five years of full-time work as a biostatistical consultant
experience as the lead data analyst/statistician on many applied projects
leading some of my own research papers
participating in statistical methodology papers

I decided the main weakness of my application would be my lack of mathematics background. Even though I had good grades in my Biostatistics MS program, I had only the minimum math requirements to apply to that program originally (Calc I-III and linear algebra), and my Calc III and linear algebra grades were mediocre, albeit from 10 years ago.

Of note, when I read posts on GradCafe, the general consensus was that U.S. citizens (which I am) have a higher chance of being admitted to programs in the U.S. I don’t have much insight on this, but I think it has to do with funding opportunities. There are many government sponsored funding opportunities which are unfortunately only available to domestic students.

In the end, I applied to eight schools: two schools in New York City (where I currently live) and six other schools which are consistently considered to be top programs for biostatistics. Although I’m well-aware that rankings are imperfect measures of the quality of programs and there are many excellent biostatistics programs that are not top-ranked, I also knew I would only consider leaving NYC and my full-time salary for a few opportunities.

Application organization

I kept track of all my applications and notes on a Notion page. I made several tables with information about each school’s requirements and created to-do lists for various tasks (e.g. send transcripts). I also wrote out my letter of recommendation (LOR) writers’ names, emails, and titles so that I would have an easier time copying and pasting.

Application Components

Transcripts.

All the schools I applied to required me to submit unofficial transcripts and then manually enter all relevant (science, math, statistics, etc.) coursework into their own application system. I had to enter the course name, course number, number of credits, semester I took it, and grade for each course. This is super time consuming, and I recommend beginning to work on this as soon as applications open. Many of the application portals were glitchy, and this would have been hard to complete at the last minute.

My GRE scores expired a few years back, but thankfully all the schools I applied to haven’t required them since the pandemic, so I didn’t retake the test. Of note, a few schools said they required them on their website, but did not actually when I inquired with admissions. For one school I only had to self-report my old scores.

Letters of Recommendation

All schools required three LORs, and most accepted up to four or five. The people I asked to write my LORs were:

A long-time colleague and mentor who could speak to my research potential for both methods and applied work. They are mid-career and known within the field of causal inference statistical estimation methods, which is what I want to continue studying.

A long-time physician collaborator who I’d also worked with on applied projects for 4 years who could talk about my skill set in applied projects.

My current boss, an academic epidemiologist with strong training in statistical methods. At the time I’d only worked with them for a few months, but they seemed comfortable writing about my scientific potential.

(Extra letter) My former professor 1 from my applied capstone course during my MS. They are late-career and well-known within the field of biostatistics. They confirmed they could speak to my discipline and aptitude for completing coursework.

1 Some schools explicitly request a LOR from a former professor.

I think this is general LOR advice, but I only asked individuals who I was pretty sure would write strong letters on my behalf. I tried to strike a balance in people who were recognizable names within the field of biostatistics and who worked with me enough to write about me. Remember to ask your letter-writers early, as well as register early with the schools’ application systems so the writers have plenty of time to upload their letters.

Curriculum Vitae

All of the schools required me to submit a Curriculum Vitae (CV) document. This is the version I submitted for applications. Depending on your background, sections will look different. I recommend having someone within academia, preferably (bio)statistics or related, review your CV. If you are a student, you should also take advantage of your university’s career center resources to review.

Additional feedback I received for this which may be relevant to someone else:

List out all details/roles for classes you served as Teaching Assistant.

List out blog posts under “Scientific Communication” and try to illustrate their impact. I’ve been blogging for years and have a Google Analytics attached to my site, so I was told to add the number of views.

The Statement of Purpose (SOP)

This was by far the hardest part of the application for me! There’s a lot of opinions surrounding the statement of purpose for Biostatistics PhDs, from, “it’s very important and the only way to set yourself apart to the application committee,” to, “nobody reads it and it won’t affect your application.” I opted to believe the first set of opinions and took my SOP seriously.

I received a lot of advice on my statement. The most helpful piece of advice I received was that the SOP is not about highlighting qualifications – that’s what the CV does – and qualifications alone do not equate to success in academia. You need drive and motivation, and your SOP is the chance to show that you have it. It is more about your philosophy and research goals than stating what you’ve done so far. Every time you bring up an accomplishment, you should explain to the committee exactly why that’s relevant to your overall goal of pursuing a PhD in biostatistics. If something is not directly relevant to why you want to pursue a PhD or why you’ll be a successful researcher, you should not include it.

I ended up receiving so much advice for this that I decided to publicly post my UW SOP on a Google Doc with comments. Some other resources I found helpful include these California State Example Essays and Lucy Lai’s Personal Statement for her Neuroscience PhD applications . These tweet threads were also useful:

Writing a statement of purpose (SOP) for PhD admissions – please do not make me read another “as a kid, when I looked at the sky…” a thread. — Hadas Kress-Gazit ( @HadasKressGazit ) November 15, 2021

Every year I read a lot of grad school applications from accomplished people that don't give me the info I'm looking for. It feels like a major hidden curriculum thing. So here's (my opinion on) how to write a great Statement of Purpose/Research for a PhD program. 🧵 1/ — Roman Feiman ( @RomanFeiman ) October 27, 2022

If you take only one thing away from my SOP advice: start writing your SOP early and ask at least one person who has served on an academic application committee, preferably for Biostatistics PhDs, to read your draft to make sure you’re on the right track . This is the easiest part of your application to control!

The Personal Statement

Only a few schools required this, and the prompts were related to why your background uniquely adds to your scientific potential. This statement is, of course, very personal to your own background! I wrote about how growing up in a rural Midwest town with my family in blue-collar jobs shaped my understanding of public health and access to education. I also wrote about my work and volunteer experience in low income areas and with underrepresented groups, and how my motivations for improving diversity in the field are driven by my experiences as an underrepresented gender in STEM. This will obviously look very different for any given applicant. I am not posting my personal statement publicly, but if you have a reason you think it’d be helpful to see my personal statement, please email me.

Application Fees

Almost every school had an $80-130 application fee, paid upon the time of submitting. Make sure to reach out to schools if you have any justification for receiving a fee waiver!

Pre Application Review Service (PARS)

I sent all my application materials in November to UW’s Pre Application Review Service (PARS) for review by current students. This is an excellent service available to underrepresented genders and minority groups. Not only was I able to get feedback on my application, but I made connections with a statistics PhD student who reviewed my application and a biostatistics PhD student who he subsequently introduced me to via email.

Our department is offering a pre-application review service (PARS) initiative to provide support and mentorship to PhD applicants from historically marginalized groups. See details here: https://t.co/0evhEigqrm pic.twitter.com/xZ0B8LA8Gt — UW Statistics ( @UWStat ) September 27, 2022

Interviews and visit days

Applications were due December 1, and I began hearing about interviews the first week of December. My first interview was mid-December (a virtual half-day). The first in-person interview was in mid-January. My last interview was late February (virtual) and in-person visit days for admitted students continued through early April. All in-person visits except for one school were fully funded. I did not attend the unfunded visit day.

I found the interviews stressful to plan around because each was announced only a few weeks before the day(s) of the interview. I had a lot of anxiety leading up to each interview, however, the questions themselves were easy to answer (with the caveat that I’ve done many interviews and interviewed several biostatistician job candidates over the years, so I had an idea of what to expect). The questions I received were generally along the lines of:

Tell me about why you want to do a PhD. Why do you think you need it? What are you hoping to accomplish during and after your PhD?
Why [this school]? Why [city the school is located in]?
Tell me about a time when you had to collaborate with others to get a project done.
What questions do you have about our program or [location of the school]?

For many of my answers, I reiterated (sometimes verbatim) sentences from my statement of purpose. I also brought up different research projects I’d done over the years, depending on where the conversation went. The people interviewing you are, above all, trying to assess your fit with the program. I know it is easier said than done, but my takeaway was that it was best to just let the conversation flow. The interviews were usually 30 minutes long, so make sure you have lots of questions prepared for when the interviewer flips the question-asking to you. If you run out of questions about the program, start asking the interviewer about their research or what their favorite things to do are around the university.

Despite the interview questions feeling straightforward to me, the days were EXHAUSTING, both physically and mentally. You essentially have 8am-9pm day(s) with other applicants, students, and faculty, and you have to be “on” the entire day. This experience can be really overwhelming, so go easy on yourself. I recommend writing notes down after each interview day/visit – I kept a long running note on my phone.

Decision time

Of the eight schools I applied to, I interviewed at five and was accepted to five. 2 My acceptances and rejections didn’t make a ton of sense to me either way, meaning I was surprised to receive certain acceptances while also receiving rejections from schools I thought I may have a higher chance of getting into. This supported a phrase I heard a lot, “PhD admissions are a bit of a black box.” There are many qualified applicants, and it is hard to discern between applicants by a CV, transcript, and a statement of purpose. Different application committee members will have biases in what they’re looking for (e.g. strong mathematics background vs. research experience) and it’s best not to dwell too much on any particular outcome.

2 If you are applying and think it would be helpful to know which schools I applied to and/or what my experiences were at each, please email me.

It was a difficult decision for me to choose between programs. I was extremely torn over the idea of leaving the community I have in NYC. However, I could not shake the feeling that my visit to UW had felt overwhelmingly “right.” After a lot of pro-con lists, I decided to go with this gut feeling.

The major choices which affected my decision were location, overall fit of the program/coursework, current students’ relationships within and between cohorts, perceived work-life balance of students and faculty, funding/teaching/research requirements, stipend amount, and number of faculty working on what I wanted to work on (non-parametric causal inference). After I finished my visit days, I set follow-up meetings with professors and the graduate program directors from multiple schools to make sure I understood my options correctly. I made my decision to attend UW at the end of March, about two weeks before the April 15th decision deadline.

Miscellaneous notes

A few miscellaneous details I learned and thoughts I had throughout the application process:

Admission rates

I found it difficult to find admission rates online, but the numbers given at some of my interview/admit days (if I remember correctly) were approximately :

250 or so applicants
15-25 interview spots
7-20 spots in the cohort offered

The final number of spots in the cohort and process for obtaining that number varied quite a bit by school. A few schools ranked candidates and could only offer a spot to the top 6-7 candidates. Once someone rejected their offer, they moved down the list to offer the next candidate. Other schools accepted a large (~20) number of applicants with the expectation that only a certain percentage would accept their offer. Finally, at least one school I applied to could only offer a fixed number of spots (12), and could not re-offer to another applicant if someone turned down their offer. That school was careful to only give offers to those they really thought might attend.

Most Biostatistics PhD programs will only admit students if they know they can fund them, i.e. pay for tuition and a stipend, for 4-6 years. For the programs I was admitted to, the stipend offers ranged from $36-46,000 per year, pre-tax. A PhD stipend is often described as “enough to live, but not enough to save,” although this will obviously vary by the city’s cost-of-living and the student’s personal financial situation.

I said earlier that a PhD is a huge financial commitment, and the stipend is the main reason why. Even though the amount of money might seem like a lot (it did to me when I was going through my MS degree!), the time you’ll spend earning your PhD is undoubtedly a short-term loss of potential earnings. If you have a strong quantitative background (as most Biostatistics PhD applicants do), a conservative estimate is that this loss could accumulate to over $400,000 in pre-tax income. 3 This estimate is not accounting for the compounding interest you will miss out on in retirement savings (assuming you would put money towards retirement if working full-time). Although the earning potential is higher with a PhD than with an MS, it will still take some time to counteract the short-term loss.

3 My calculation for this is (potential salary - stipend) * expected years in PhD.

On that note, if you have multiple funded offers, it is worth asking each program what their policies are regarding internships, part-time work, and freelance consulting work, because all of these are supplemental sources of income. Are any of these types of work allowed or encouraged, and does participating in them affect the stipend amount you receive (beyond potential differences in tax brackets)? The answers vary by program, and sometimes even by student due to differences in funding sources.

Reaching out to professors in advance

I did not email any professors before applying, so I unfortunately don’t have much to share on this topic. I doubt it would’ve helped me get into any additional programs, but who knows! It definitely has the potential to be informative and a good networking experience. Lucy Lai includes a template for reaching out to professors in her blog post , as does John Muschelli in his post, “Some things I wish I knew about Grad School” .

Looking ahead, preparing for my PhD coursework

UW is on the quarter system, so I’ll start classes with a cohort of eight other students at the end of September. This summer I’ve been working, enjoying life sans homework, and trying to remember all the math I’ve forgotten over the years.

I’m refreshing myself on linear algebra using a combination of Khan Academy (I love Sal’s visualizations – I listen on 1.5-2x speed and slow down when he says something that I don’t understand) and Linear Algebra Done Right by Sheldon Axler. The latter is a small textbook which is meant to be a second learning of linear algebra (it’s quite abstract compared to the usual sequence of teaching the subject). This Github repo with solutions to Axler’s exercises is also helpful. I am also brushing up calculus using a mix of Khan Academy, random YouTube videos, and the textbook Advanced Calculus by Patrick M. Fitzpatrick. This is what I wish I would’ve done before starting my MS degree. 🙂

If you’d like to know more about what my work looked like as an applied biostatistician in medical research, please see my Day in the Life of a Biostatistician post. I answer common email questions stemming from that post in this Follow-up post . As always, feel free to email me with questions, clarifications, or suggestions for additional resources to include.

Until next time!

Acknowledgments Deciding to start a PhD program was a huge decision for me, and I am grateful to so many for encouragement and advice over the years. Thank you to my colleague Iván Díaz , who has taught me an enormous amount over the past five years and who has been instrumental in my development as a researcher. Thank you also to my former professors, especially Tom Braun for convincing me not to drop out of my Biostatistics MS program during my first semester :), and Bhramar Mukherjee for consistently vocalizing her belief in my potential. Finally, thank you to Elizabeth Sweeney , Sam Adhikari , David Lenis , Kara Rudolph , Alejandro Schuler , and Seth Temple for helpful conversations which contributed in various ways to information I’ve shared in this post.

Graduate Programs in Biostatistics

Prospective Students

Admissions Overview

The Division of Biostatistics and Bioinformatics in Herbert Wertheim School of Public Health and Longevity Science seeks highly motivated applicants with a strong mathematical background for our Doctoral program in Biostatistics which started in Fall 2016 and our new Master of Science in Biostatistics which started in Fall 2019. Our programs aim to train the next generation of biostatisticians in the mathematical theory, computational skills, and inferential methods needed to analyze complex biomedical data. The programs will provide:

An integrated, comprehensive, and intensive didactic curriculum in biostatistics, statistics, and the mathematical foundations needed for analysis of biomedical data;
Rigorous training in computing, reproducible research practices and data management skills;
Substantive training in a specific domain of application in biomedical or public health sciences;
Instruction on research ethics, protection of human subjects, and confidentiality requirements;
Experiential learning and mentoring in communication and scientific writing skills and the ability to work in interdisciplinary teams.

New for Fall 2024 Master's Application!

Fall 2024 applications dates, applications for both programs will open on september 20, 2023. , the application deadline for the doctoral program is january 3, 2024, the application deadline for the master's program is january 17, 2024 . , an application to either program may be submitted here., ms in biostatistics, ms admission requirements.

Interested candidates should prepare the following application documents to be submitted online:

Statement of Purpose (details provided below)
Transcripts (official transcripts will be required if admitted into the program); GPA >= 3.0 required for admission.
List of mathematics and statistics courses taken/projected as well as textbooks used and grades received (can be in table format)
Three letters of recommendation; at least two should address academic and/or professional qualifications for pursuing a Masters program in biostatistics. If more than three are received, the first three received will be reviewed.
TOEFL test scores (for international applicants from non-English speaking countries)
MS Applicants Who Are U.S. Citizens are required to complete a FAFSA . This will assist in determining which applicants are eligible for grants that may have been awarded to support the interdepartmental program by various agencies and donors. FAFSA information plays no role in admission decisions.
Curriculum vitae

Statement of Purpose

Your statement of purpose and objectives (500-1000 word limit suggested) should address all of the following items:

Describe those experiences that have shaped your interest in biostatistics.
Outline your professional goals, both immediate and long-term.
Discuss the strengths you bring with you, including work and other experiences which you feel makes you more likely to succeed in this program.
Discuss why you are interested in the Biostatistics MS program in the Herbert Wertheim School of Public Health and Longevity Science (Division of Biostatistics and Bioinformatics, UCSD)
Describe the one academic experience that has meant the most to you in your educational career.

Application Deadline

The 2024 admission application will be open from September 20, 2023 to January 17, 2024 for MS and until January 17, 2024 for the PhD application. If you wish to be considered for both the PhD and MS program, you must apply by January 3, 2024.The program matriculates MS students in the fall only.

Application Fee

The application fee is $135.00 for U.S. Citizens and Permanent Residents or $155.00 for International Applicants. For more fee information, please view the Graduate Division webpage here. Please note that the Division of Biostatistics and Bioinformatics does not waive this fee.

PhD in Biostatistics

The PhD program is distinguished by its high mathematical content and rigorous technical training in theory and methods, as well as, training in practical collaborative skills, and use of important data from many areas of the biomedical sciences. We expect to provide a stipend and tuition remission to qualified students for the duration of their studies

PhD Admission Requirements

Three letters of recommendation; at least two should address academic and/or professional qualifications for pursuing a PhD program in biostatistics. If more than three are received, the first three received will be reviewed.
General Graduate Record Examination (GRE) test scores
PhD Applicants Who Are U.S. Citizens are required to complete a FAFSA . This will assist in determining which applicants are eligible for grants that may have been awarded to support the interdepartmental program by various agencies and donors. FAFSA information plays no role in admission decisions.
Discuss why you are interested in the Biostatistics PhD program in the Herbert Wertheim School of Public Health and Longevity Science (Division of Biostatistics and Bioinformatics, UCSD)

The 2024 admission application will be open from September 20, 2023 to January 3, 2024. The program matriculates PhD students in the fall only.

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Apply for a PhD in Biostatistics

The application for the Doctor of Philosophy program in Biostatistics is administered by the Horace H. Rackham School of Graduate Studies. Applications for the Fall 2024 semester are open through December 1..

Those applying for the Doctor of Philosophy program should complete the following:

online application form
all domestic applicants will receive an automatic fee waiver
three letters of recommendation (submitted online)
a statement of purpose
a personal statement
A scanned/electronic copy of the official transcript will be used for initial review by our admissions committee. You will be required to have your official transcript sent to the Rackham Graduate School only if you are recommended for admission.
International students whose first language is not English must also submit TOEFL scores.

Michigan Public Health does not require and does not review GRE or other standardized test scores for admission to any master's or doctoral programs. Applications will be reviewed holistically based on required application components. Please contact our admissions staff at [email protected] if you have questions.

Minimum Requirements

Applicants with either a relevant master's degree (i.e. a graduate degree comparable to our MS in biostatistics) or strong candidates with a bachelor’s degree will be considered for PhD admissions.
three semesters of calculus
a course in matrix or linear algebra
an introductory course in statistics or biostatistics
students with less preparation in mathematics or statistics may be conditionally admitted.
Please refer to the Admissions page for international applicants for details on English proficiency requirements and other requirements specific to international students.
The code for the TOEFL is 1839
All applicants must upload a scanned copy, front and back, of their official transcript/academic record issued by the Registrar or Records Office, to ApplyWeb for each bachelor’s, master’s, professional, or doctoral degree earned or in progress. The scanned copy of the official transcript is used for initial review by the graduate program faculty. After an offer of admission is made, an official transcript must be submitted to Rackham.
Please refer to Rackham Graduate School guidelines when submitting transcripts.

Current online grades policy

Courses must be:

taken at an accredited university and successful participation is documented by a grade on the transcript or by a written communication from the instructor; OR
followed by more advanced coursework (for example, multivariable calculus is followed by real analysis) at an accredited university and successful participation in the more advanced course is documented by a grade on the transcript or by a written communication from the instructor

Courses without grades or evaluation of class performance by the instructor typically will not satisfy program admissions requirements.

The duration of this relaxation of departmental policy may be extended depending on the duration of the COVID-19 pandemic.

Frequently Asked Questions

Contact information.

Ph.D. Program Coordinator: Nicole Fenech Email: [email protected] Telephone: 734-615-9817

Department of Biostatistics School of Public Health University of Michigan 1415 Washington Heights Ann Arbor, MI 48109-2029 Fax: 734-763-2215

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Doctoral Programs

Biostatistics.

Biostatistics applies statistical and probability theory to human health and disease. The PhD program in biostatistics prepares individuals to develop or adapt statistical methods for solving problems in the health field. Students enjoy extensive library and computer facilities, as well as myriad opportunities for involvement in numerous research activities in the biomedical sciences and clinical research, which often lead to dissertation topics.

The department awards a number of fellowships to recognize academic achievement and support future scholarly success. As teaching and research experience are considered an important aspect of the program, these fellowships include some teaching and research apprenticeship.

Admissions Requirements

While many of the applicants admitted to Columbia’s PhD program in biostatistics have already completed (or are completing) master’s degrees in biostatistics, statistics, or a related field, admission is open to well qualified students holding (or completing) bachelor’s degrees. Those admitted with a bachelor’s degree are typically strong students from programs that emphasize a rigorous background in mathematics and/or statistics.

Depending on prior training and background, students may be required to take additional master’s level course work in the Mailman School of Public Health as part of their PhD training.

In addition to the requirements listed below, all applicants must submit an official transcript from each prior institution, a statement of academic purpose, and three letters of evaluation from academic sources. All international students whose native language is not English or whose undergraduate degree is from an institution in a country whose official language is not English must submit Test of English as a Foreign Language (TOEFL) or IELTS scores.

Deadline for Fall Admission: December 1
Deadline for Spring Admission: No spring admission
Resume/CV: Yes
Writing Sample: No
GRE General: Optional
GRE Subject: No

View competencies, course requirements, sample schedules, and more in our Academics section.

Paul McCullough, Director of Academic Programs

Biostatistics Graduate Program

Application process.

Ideal applicants hold a bachelor’s degree and have taken three semesters of college calculus (through multivariable calculus), one semester of linear algebra, and a class in introductory statistics. Applicants must submit a CV, a statement of purpose detailing their interest in biostatistics, transcripts, GRE scores, and three letters of recommendation. Applicants are encouraged to highlight their strong quantitative and analytical potential and communication skills (verbal and written).

The following documents are required for your application to be complete:

We held an info session, “ Improving Your Odds: Optimizing Your Vanderbilt Biostatistics Application ,” on November 9, 2023. Click the link to access the video.

Graduate Programs

Biostatistics.

The doctoral program in Biostatistics provides the training necessary to carry out independent research in theory, methodology and the application of statistics to important problems in biomedical research, including research biology, public health and clinical medicine.

The Ph.D. program is administered by an active, expanding and highly interdisciplinary faculty in the Department of Biostatistics. Major areas of research activity include Bayesian inference, analysis of biomarkers and diagnostic tests, causal inference and missing data, time series and functional data analysis, modeling of social networks, bioinformatics, longitudinal data, and multilevel modeling. Faculty collaborate actively with investigators in the areas of cancer prevention and screening, behavioral sciences, HIV/AIDS, health care policy, genetic epidemiology, neuroscience, and genomics.

Additional Resources

All PhD graduate students are provided with a new laptop computer and office space. Students also have access to the computing infrastructure at the Center for Statistical Sciences, a high-end, continuously updated computing environment featuring both Unix and PC/MAC networks, with access to all major software for data analysis and numerical computing. CSS also maintains a considerable collection of statistics texts and journals in the Walter Freiberger Biostatistics Library.

Application Information

MCAT or LSAT tests cannot be substituted for the GRE. Applicants to the Ph.D. program should have taken courses in calculus (three semesters), and advanced undergraduate courses in linear algebra and probability. Experience with numerical computing is also recommended. Applications from students in applied fields such as biology, biochemistry, economics, and computer science are strongly encouraged, with the understanding that necessary mathematical coursework may have to be completed before or soon after enrollment in the program.

Applicants to this School of Public Health program should apply through SOPHAS , a centralized application service for accredited schools and programs in public health. Brown University School of Public Health GRE reporting code: 7765.

Application Requirements

Gre subject:.

Not required

GRE General:

Official transcripts:, letters of recommendations:.

(3) Required

Personal Statement:

Additional materials:.

Application Fee

Additional Requirements:

International applicants.

Language Proficiency (TOEFL or IELTS if applicable)
Transcript Evaluation (if applicable)

Dates/Deadlines

Application deadline, completion requirements.

For all Ph.D. students, 24 credits are required of students matriculating in the program without a master's degree; 16 are required beyond the master's. For those with a related master's degree, up to eight units can be transferred. Both written and oral exams, plus a dissertation comprising an original contribution to the field, also are required. Students are expected to participate in academic activities such as the Statistics Seminar and faculty–organized working groups.

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Department of biostatistics, mailing address.

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Degrees Offered

PhD in Biostatistics

Description.

The doctoral program in Biostatistics trains future leaders, highly qualified as independent investigators and teachers, and who are well-trained practitioners of biostatistics. The program includes coursework in biostatistics, statistics, and one or more public health or biomedical fields. In addition, successful candidates are required to pass PhD applied and theory exams and write a dissertation that reports the results of new biostatistical research undertaken by the candidate.

Likely Careers

Clinical medicine, epidemiologic studies, biological laboratory and field research, genetics, environmental health, health services, ecology, fisheries and wildlife biology, agriculture, and forestry.

Applicants usually have a degree in mathematics, statistics, or a biological field. All applicants should have the equivalent of 30 or more quarter credits in mathematics and statistics, including linear algebra, probability theory, and approximately 2 years of calculus.

Concurrent Option: PhD/MD

Application Deadline: Dec 1 - Autumn Quarter Entry

Competencies

Upon satisfactory completion of the PhD in Biostatistics, graduates will be able to:

Meet the learning objectives of the MS program in Biostatistics ;
Recommend and defend appropriate choices of methods to analyze independent outcome data;
Implement non-standard statistical methods accurately and efficiently;
Provide rigorous proofs characterizing the properties of standard statistical methods;
Consult effectively with other scientists, addressing statistical issues in the design and analysis of public health or biomedical studies; and
Design and carry out biostatistical research that will propose a new statistical method or will provide new information about the properties of existing methods.

Learning objectives for the PhD program in Biostatistics in the Generic Pathway: Upon satisfactory completion of the PhD program in Biostatistics in the Generic Pathway, graduates will be able to:

Recommend and defend appropriate choices of methods to analyze longitudinal, clustered and other non-independent outcome data;
Develop expertise in an area of biostatistical methodology; explain the strengths and weakness of different statistical methods in that area; and
Explain both orally and in writing how advanced statistical methods work, assessing their strengths and limitations, and the place of particular methods in the larger statistical literature.

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School of Public Health

A nationally top-ranked institution that offers a superior array of advanced degrees and is home to world-renowned research and training centers.

Degrees & Programs
Doctor of Philosophy (PhD)

Biostatistics PhD

Major Coordinator Sally Olander 612-625-9185 [email protected]
Director of Graduate Studies [email protected]

Extract and communicate meaning from complex biomedical data.

A biostatistician is an important part of many research teams. Working in close partnership with researchers across a wide array of scientific disciplines, a biostatistician designs studies and develops statistical tools to extract meaning from complex data.

With a biostatistics PhD, you’ll conduct original research, collaborate and consult with biomedical researchers, implement and disseminate results of this research, and teach and mentor others in this field.

Program Brochure

Biostatistics PhD Profiles

It’s been a unique experience being able to apply my data science knowledge [at Netflix].

John Ssenkusu

Biostatistics, PhD '18, is passionate about the potential to harness data to improve health.

Carlos Serrano

Originally I was planning to go to medical school. In fact, I applied before coming here.

Advantages of the Program

Personal Attention. The PhD student-to-faculty ratio is approximately 1.5:1, one of the lowest of any biostatistics program in the nation.
Impact. The Division of Biostatistics & Health Data Science (BHDS) plays a leadership role in many national and international clinical trials, including the first vaccine trial for Ebola and the largest HIV/AIDS treatment trial in history .
Breadth. Interdisciplinary research includes collaborations across the University of Minnesota with the Medical School, College of Veterinary Medicine, the Carlson School of Management, the Humphrey Institute for Public Affairs, the Supercomputing Institute, and Minnesota Population Center.
Productivity. PhD students graduate with at least one peer-reviewed publication before graduation; many have three or more.
Placement. Graduating students have gone on to faculty and postdoctoral positions at top research universities, as well as research leadership positions at government agencies and in private industry.

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The admission essay ("the statement of objectives") is a vital piece of information. It provides the Admissions Committee with information not available elsewhere in the application. Here are three critical questions that the written statement answers:

1. is this candidate capable of effective written communication; 2. does the candidate convey a sense of thoughtfulness and maturity about the chosen area of study; and 3. what is the applicant's anticipated career plan? This third piece of information is, in most cases, the most important for two reasons.

First, it allows the Committee to determine whether a good fit exists between what the applicant plans to do, and what the department is capable of providing.
Second, it indicates to the Committee that the applicant has the necessary intellectual and personal maturity to pursue graduate study.

1. Read the instructions for the written statement carefully and follow them.

Do not exceed the 2-page limit: do not shrink the font size to fit the 2-page limit. Eleven or twelve point font and two pages is sufficient for meeting the requirements of the essay.

2. Specify the degree program and track of research interest from the offerings of the department.

We offer the PhD doctoral degree, the MHS and ScM masters’ degrees, certificates and non-degree post-doctoral fellowship training. Please select from the following Research Tracks: Epidemiology of Aging, Cancer Epidemiology, Cardiovascular and Clinical Epidemiology, Clinical Trials and Evidence Synthesis, Environmental Epidemiology, General Epidemiology and Methods, Genetic Epidemiology, and Infectious Disease Epidemiology. The Certificates in Clinical Trials, Healthcare Epidemiology, Pharmacoepidemiology, and Epidemiology for Public Health Professionals are also accepting applications.

3. Avoid extraneous personal or biographical information that does not inform the committee about future career plans.

A high percentage of applicants begin their essay with a personal anecdote about a personal health event, a trip abroad, or an account of illness in the family. While this does allow the committee some idea of the applicant's motivation, it should not be the whole essay. Please keep the anecdote to 1/3 or less of the full essay. One paragraph is usually sufficient to communicate one's motivation.

4. Emphasize what you will do, not what you have done.

Most of the relevant information about what an applicant has accomplished is easily and rapidly accessible to the committee through a review of the CV and other application materials. Many applicants use 90% of the space in the statement to restate and embellish those items. Additionally, in the final paragraph, most applicants will give passing attention to the main objective of the statement (to articulate a clear and concise plan for progressing toward a career in a given field). As a general guideline, more than half of the essay should be spent explaining what the applicant intends to do during and after graduate study.

5. Provide evidence that you as an applicant are well matched to the interests of the department.

Some applicants engage in “name dropping” subsequent to a review of catalogues and web sites. However, faculty members do change schools or areas of research. A well-written statement explains how particular faculty, research programs, or course work is particularly well-suited to meeting the training objectives of the applicant. Additionally, if the only faculty member doing the research discussed leaves the program, the Department cannot in good conscience grant admission to the program.

6. Be as concrete and specific as possible about your interests and proposed course of study.

An applicant's failure to articulate a clear and detailed training plan leaves the Committee with the impression that the applicant has not thought through the nature and meaning of graduate training, and may not be ready for admission. These are the questions to address: (in Epidemiology for instance) How will the applicant help rid the world of disease? To what end will skills and knowledge be directed? What specific aspect of a broad domain of work holds the applicant’s interest? And finally, the statement of objectives is not a binding document. Students, once they matriculate, often shift and refine their focus of study. No one is obligated to remain faithful to the plan they articulate. However, the statement of objectives is designed to provide the department a strong understanding of the applicant's motivation and commitment to the field and a clear indication of the applicant's writing ability.

Admissions Deadlines: Doctoral (PhD): December 1 Masters (MHS & ScM): January 15 Certificates: September 1 Non-degree post-doctoral fellowship: minimum of 90 days prior to the start of training

Biostatistics Personal Statement

A statement of purpose statistics masters should indeed be produced according to specific guidelines to minimize things and errors in the personal statement appealing to the selection committee. If you create a stellar personal statement, your chances of being chosen and furthering your academic knowledge and experience skyrocket.

In this article, we’ll discuss how to write a statement of purpose for statistical consultant , some important tips, and samples to help you in writing an excellent personal statement. So let’s begin!

How To Write A Statement Of Purpose For Biostatistics Masters?

Well, the statement of purpose is a personal statement you write to get admission to a university.

The statement of purpose must persuade audience instructors on the admission review panel that you have a strong track record of accomplishments demonstrating your potential for graduate school success. Consider your quest statement to be a four-part structure.

Introduce yourself, your passions, and your reasons for being here.
Write a summary of your undergraduate and graduate experiences. You did some research—anything intellectual outside your academic responsibilities, such as an essential article or research project you accomplished. Mention your professional experience if you have.
Analyze the significance of your recent and ongoing efforts.
Describe your academic interests in more detail. Choose a topic that fascinates you. Finish your statement positively, expressing your eagerness to tackle the difficulties ahead.

It’s Good To Know: Podiatry Personal Statement

Tips To Write A Biostatistics Personal Statement

Below are some of the essential tips for writing a biostatistics personal statement.

Discuss Your Long-Term Objectives And Desires.

When writing a Biostatistics Personal Statement, this is among the most significant elements to consider. On the pitch, sharing your dreams, hopes, and future goals is a terrific approach to increase your chances.

Also, if you can include some background about how you acquired Statistics or Mathematics in the past, how it has helped you afterwards, and how you believe it will benefit you in the future, it is an excellent approach to differentiate yourself, for example.

Furthermore, the significance of this art is to demonstrate that you understand what you’re applying for or what you’re applying for it. If you understand something unique about the program that excites you, such as a fantastic professor, a subject in which you are passionate about, professional groups, or even just your overall program interests, you must demonstrate it in your statement to improve your chances.

Make Yourself Recognized.

It is critical that you demonstrate your passion for being a member of the Biostatistics program in your Personal Statement Biostatistics. It’s also critical that you demonstrate your expertise and command of the topic by citing specific accomplishments such as research, projects, works, employment positions, or anything else you’ve done previously that gives you a better fit for the program and deserving of the opportunity.

On the other hand, you must use an engaging style of writing to express your enthusiasm for this program, making yourself an excellent and highly fascinating alternative for the selection committee . To improve your prospects, tell stories, offer anecdotes, show your experience, and give some examples of your global standard.

Write professionally. It may seem self-evident, but many individuals overlook the fact that while writing, one of the most important factors considered by a selection committee independent of the career of your choice is your syntax, spelling, and technicalities.

There’s a good chance you’ll be denied if you don’t follow grammar rules, avoid spelling errors, or simply don’t learn how to talk in the appropriate language. Writing like an expert needs more focus on your grammar, tone, and style.

Leisure time paragraph

Biostatistics/Bioinformatics Personal Statement Examples

The below samples are just for taking ideas on how to write a personal biostatistics statement.

“All data is useless until you get something out of it,” one of my favourite college professors used to remark. It wasn’t until I started investigating that I understood how accurate that remark was. In the highly competitive field of science, errors in grouping and sample selection might invalidate an entire experiment. Therefore accuracy is critical. Via the introduction of the Bettman-Sykes statistical test, Phoenix University has reinvented the discipline of biostatistics. Still, it is this spirit of creativity that drew me to Phoenix for my doctoral studies.

All experimental proof for a hypothesis is based on examined quantitative data. In my sophomore year, I used flow cytometry to evaluate over 300 samples and thought I experienced an uptick in mean channel fluorescence. My lab mentor, on the other hand, categorized my intensities by tissue ID, and my results were no longer favourable. Since that day, I’ve dedicated my life to conducting precise statistical studies. I have to evaluate the analyses completed during various experiments and judge the statistical accuracy of the test used as a member of my university’s Biology department’s Committee on Statistically Significant.

My graduate career goal is to assist in ensuring the integrity of scientific research. The Biostatistics program at Phoenix University is ranked among the best in the country, and I’m confident that the statisticians here have a thorough understanding of not only science but also how statistics play an important role in scientific discoveries. For an ever-more-specialized scientific business , I’d like to produce ever-more accurate and adaptable statistical tests. In addition, I’d like to evaluate the reliability of the present test error ranges. After all, if the results aren’t accurate, it’s as if there weren’t any results at all.

With the rapid technological advancements of the twenty-first century, an increasing number of fields have grown to rely on the creation and processing of massive amounts of data. Biology’s ability to create meaningful findings from enormous pools of data has been dramatically enhanced by new technologies, making them more accurate and valuable to the general population. Large amounts of data, on the other hand, necessitate effective ways of analyzing and drawing conclusions. Since high school, I’ve been interested in biostatistics, and I’m hoping this Master’s program in biostatistics will help me pursue my passion and advance my career.

I was employed part-time after college for a campus lab that analyzed human tumours to uncover novel mutations that cause certain diseases. Following data collection, rigorous statistical analysis was required to determine which variants were responsible for the disease, which were just variances from sample to sample. To make this situation worse, differences in sample processing daily accounted for inconsistencies in sequencing data, making analysis challenging. I aim to study strategies for stratifying data as part of my doctoral program to improve the robustness of my analysis.

Another of the main reasons I chose biostatistics as a job is because I appreciate the exposure it provides to scientific research. From cancer to stem cells, biostatistics touches almost every element of scientific inquiry. I’m excited to see what progress may be made by making massive databases more accessible to researchers worldwide. I’m thrilled to be a part of the revolution, and I’m looking forward to it now as a graduate student in biostatistics.

The methods for assessing the outcomes of scientific study have evolved along with it. The amount of data collected rose as technology became more incorporated into scientific study. As scientific problems are becoming more complex, additional data is required to validate a hypothesis. Statistics, on the other hand, must be performed to conclude raw data. I am interested in a career in biostatistics as a mathematician who also enjoys the living sciences. Still, I am confident that this graduate degree program in biostatistics will qualify me for admission into another field.

We were required to work with yet another school on campus and develop a project of our choice as a subject in the curriculum. I chose to contact Dr Emily’s group at the Biology department, which specializes in cardiac illness research. What started as a three-week assignment developed into four-year cooperation wherein I ran several statistical algorithms for their laboratory and created 3-D data visualizations. The medical applications of their study piqued my curiosity, and it prompted me to pursue biology courses from outside my specialization.

I’m hoping that this Biostatistics Graduate Program will help me improve my biological and statistical understanding so that I can draw more accurate conclusions from enormous volumes of data.

A university degree in Biostatistics, I believe, will provide me with the abilities I have to work as a statistician in the modern Life Sciences sector.

Business and management personal statement example

Final Thoughts: Biostatistics Personal Statement

Keeping in mind the above-mentioned tips, your biostatistics personal statement will look like a statistics masters personal statement . Stick to the main purpose. Don’t write any irrelevant story.

If you are facing any problem writing your biostatistics personal statement, you can hire our writers. They will provide you with the best statistics personal statement within a given deadline.

You can also ask for a sample or two that are unique and original. Feel free to approach us!

Related Queries

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Author & Editor Team: : Adila Zakir, Alexa Smith

Our review panel has been working in academic and non-academic writing for more than 1 decade.

Statement of Purpose

The Statement of Purpose and Objectives requirements vary by program. Do not underestimate the importance of this statement. It is your opportunity to inform the faculty reviewers of your qualifications, motivation, and potential to make a contribution to the field of public health.

Statement requirement for all Master of Science (SM) and MPH (excluding JD/MPH):

The Statement of Purpose and Objectives should not exceed 600 words , although SOPHAS allows for a higher word count. In your statement, please describe the following:

Academic and/or professional preparation for a career in public health
Your focused interest in the degree program/department or MPH field of study to which you are applying
Career plans upon completion of the program at Harvard T.H. Chan School of Public Health

Note any relevant strengths or weaknesses in your background or in your ability to carry out your professional responsibilities.

Statement requirement for JD/MPH:

The Statement of Purpose and Objectives should not exceed 750 words , although SOPHAS allows for a higher word count. There is no prescribed format for the essay, but it should include information about the following:

Your focused interest within the field of public health
Previous academic, professional, and/or extracurricular experiences that demonstrate your interest in public health or health-related issues
Reasons for wanting to enroll in the joint JD/MPH degree program
Career plans upon completion of the joint degree program

The essay should persuasively establish (1) your preparation to undertake this intense course of study, (2) your understanding of the MPH curriculum, and (3) a good fit between the curriculum and your interests and goals.

Statement requirement for DrPH:

In your statement, please answer all of the following questions and number the response accordingly:

Describe in detail your most significant professional experience and explain why it prepares you to pursue advanced professional studies in public health. (up to 500 words)
Provide a reflection of your current leadership abilities and describe what you seek to reinforce and/ or strengthen in context to your work, through the DrPH degree program. (up to 500 words)
Tell us about a public health problem or challenge that you seek to address as part of your studies in the program and/or upon completion of your DrPH degree program. (up to 500 words)

Kira assessment for DrPH applicants:

Once you begin your application through SOPHAS, you will have access to the Kira platform. Through the Kira platform, you’ll be asked to submit responses to several timed video and one written question. This assessment is done in an asynchronous format, so you can complete your responses on your own time before the Friday, December 1, 2023 application deadline.

Statement requirement for MHCM:

Applicants to the MHCM program who have questions about the essay requirement may contact Colin Fleming at 617-432-7075. This statement should not exceed 1,250 words.

In one paragraph or less briefly describe the organization where you are employed in terms of its purpose, its services and client population, and its scope of operation (state, county, etc., if relevant). Also provide several relevant measures of its size (e.g., annual budget and revenues; number of employees or full-time equivalents; patients or beds; and number of years in existence if less than five years old).
In one paragraph describe your role in the organization, including title/position of groups or individuals to whom you report; current responsibilities; number and types of individuals you supervise; size of budget(s) you control; and names of principal committees on which you sit.
Summarize your career development, describe the broad areas of endeavor in which you have engaged, and distinguish between clinically-oriented activities (e.g., teaching, research, practice) and leadership activities.
With reference to your present and future responsibilities and development, describe what you are primarily interested in learning as a result of participating in the program. Please note subject areas in which you are well versed and those in which you feel you need improvement.
Consider a professional project where you were responsible for design and/or implementation, and the project was successful. Describe what you learned from this experience and how it influenced you as a leader.
Consider a professional project where you were responsible for design and/or implementation, and the project was unsuccessful or not implemented as designed. Describe what you learned from this experience and how it influenced you as a leader.

Advisory Note: We recognize that cultural norms differ from country to country. In order for our applicants to be informed of what is expected, we suggest reading the following text, which is included in our Student Handbook in the section on Academic Integrity . While this text refers to work submitted for courses, it applies to the documentation submitted as part of the application process:

“All work submitted to meet course requirements is expected to be a student’s own work. In the preparation of work submitted to meet course requirements, students should always take great care to distinguish their own ideas and knowledge from information derived from sources. Whenever ideas or facts are derived from a student’s reading and research the sources must be indicated. The term ‘sources’ includes not only published primary and secondary material, but also information and opinions gained directly from other people. The responsibility for using the proper forms of citation lies with the individual student. Quotations must be placed within quotation marks, and the source must be credited. All paraphrased material also must be completely acknowledged.”

We encourage applicants to visit our page on Citation/Attributions .

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INFORMATION FOR

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Faculty of Interest

Professor of Biostatistics; Director of Medical Research, School of Public Health

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Central Nervous System Neoplasms

Associate Professor of Biostatistics, Associate Professor of Ecology and Evolutionary Biology, Associate Professor of Management, and Associate Professor of Statistics and Data Science; Co-director, Public Health Modeling Concentration

Professor of Biostatistics

Longitudinal Studies
Public Health
Sampling Studies
Randomized Controlled Trial

Research Scientist in Biostatistics

Mathematical Computing
Models, Statistical

Senior Research Scientist in Biostatistics; Director of Biostatistics in Psychiatry

Research Design
Risk Assessment

Susan Dwight Bliss Professor Emeritus of Biostatistics and Senior Research Scientist in Biostatistics; Affiliated Faculty, Yale Institute for Global Health

Lung Neoplasms
Geographic Information Systems

Assistant Professor of Biostatistics (Biostatistics)

Assistant Professor of Biostatistics

Computing Methodologies
Reproducibility of Results
Machine Learning
Epidemiologic Methods
Selection Bias
Survival Analysis
Multilevel Analysis
Comparative Effectiveness Research
Propensity Score
Observational Study
Pragmatic Clinical Trial
Mediation Analysis

Professor of Biostatistics and in the Child Study Center; Director, Regulatory Affairs Track; Affiliated Faculty, Yale Institute for Global Health; Director, Regulatory Affairs Track

Infant, Premature
Smoking Cessation
Clinical Trials, Phase IV as Topic

Professor Emeritus of Biostatistics and Senior Research Scientist of Biostatistics

Clinical Trials as Topic
Patient Preference
Pragmatic Clinical Trials as Topic

Susan Dwight Bliss Professor of Biostatistics and Professor of Cardiovascular Medicine, Yale School of Medicine; Professor, Department of Statistics and Data Science; Professor, Cardiovascular Medicine; Founding Director, Center for Methods in Implementation and Prevention Science (CMIPS); Assistant Director, Global Oncology, Yale Cancer Center; Affiliated Faculty, Yale Institute for Global Health

Cardiovascular Diseases
Health Plan Implementation
Maternal Mortality
HIV Infections
Clinical Trial
Implementation Science

Elihu Professor of Biostatistics and Professor of Ecology and Evolutionary Biology; Co-Leader, Genomics, Genetics, & Epigenetics Research Program

Bacterial Infections and Mycoses
Cell Transformation, Neoplastic
Coccidioidomycosis
Biological Evolution
Genetic Engineering
Microbiological Phenomena
Models, Genetic
Models, Theoretical
Neoplasm Metastasis
Likelihood Functions
Logistic Models
Polymerase Chain Reaction
Sequence Analysis, DNA
Nonlinear Dynamics
Molecular Epidemiology
Gene Transfer Techniques
Crops, Agricultural
Evolution, Molecular
Sequence Analysis, Protein
Gene Expression Profiling
Public Health Informatics
Microarray Analysis
Genetic Speciation
Host-Pathogen Interactions
Genetic Phenomena
Mathematical Concepts
Phenomena and Processes

Associate Professor of Biostatistics; Associate Professor, Biomedical Informatics & Data Science

Associate Professor of Biostatistics

Eye Diseases
Disorders of Environmental Origin
Pregnancy Complications
Probability
Stochastic Processes
Virus Diseases
Statistical Distributions

Assistant Professor of Medicine (Medical Oncology)

Susan Dwight Bliss Professor of Biostatistics, Professor in the Child Study Center and Professor of Statistics and Data Science, Professor of Obstetrics, Gynecology, and Reproductive Sciences; Affiliated Faculty, Yale Institute for Global Health

Child Psychiatry
Infertility
Computational Biology

Ira V. Hiscock Professor of Biostatistics, Professor of Genetics and Professor of Statistics and Data Science; Affiliated Faculty, Yale Institute for Global Health

Single-Cell Analysis
Wearable Electronic Devices
Precision Medicine
Dimensional Measurement Accuracy

Centers and other resources

Cancer Prevention and Control Research Program
Center for Biomedical Data Science
Center for Methods in Implementation and Prevention Science (CMIPS) CMIPS develops and disseminates innovative methodologic approaches to address implementation gaps and improve public health worldwide.
Center for Outcomes Research & Evaluation (CORE)
Center for Perinatal, Pediatric and Environmental Epidemiology (CPPEE) CPPEE aims, through epidemiologic studies, to investigate the impact of environmental, genetic, and clinical factors on pregnancy, birth, and childhood.
Center of Excellence in Regulatory Science and Innovation (CERSI)
Collaboration for Research Integrity and Transparency (CRIT)
Collaborative Center for Statistics in Science (C²S²) C²S² fosters collaborations involving statistical methods and technology in scientific research, for understanding disease etiologies and developing treatment and prevention strategies.
Crawford Lab
Family Accommodation Obsessive-Compulsive Disorder FAOCD refers to family members' or significant others’ participation in or facilitation of patients’ rituals and/or avoidance.
Hongyu Zhao Lab - Center for Statistical Genomics and Proteomics
Public Health Modeling Unit This renowned program uses mathematical representations of disease spread, healthcare processes, and human behavior to make better public health decisions and predictions.
Tobacco Center of Regulatory Science
Women's Health Research at Yale
Yale Cancer Center (YCC) The Center provides the best approaches for the prevention, detection, diagnosis, and treatment of cancer.
Yale Center for Analytical Sciences (YCAS) YCAS collaborative team provides expertise in the design, conduct, and analysis of health and health care studies, methodological development, and education and training.
Yale Child Study Center
Yale Claude D. Pepper Older Americans Independence Center
Yale COPPER Center: Cancer Outcomes, Public Policy and Effectiveness Research Center
Yale Depression Research Program
Yale Global Mental Health Program
Yale Network for Global Non-communicable Diseases
Yale Stress Center

Driving Innovations in Biostatistics with Denise Scholtens, PhD

“I'm continually surprised by new data types. I think that we will see the emergence of a whole new kind of technology that we probably can't even envision five years from now…When I think about where the field has come over the past 20 years, it's just phenomenal.” — Denise Scholtens, PhD

Director, Northwestern University Data Analysis and Coordinating Center (NUDACC)
Chief of Biostatistics in the Department of Preventive Medicine
Professor of Preventive Medicine in the Division of Biostatistics and of Neurological Surgery
Member of Northwestern University Clinical and Translational Sciences Institute (NUCATS)
Member of the Robert H. Lurie Comprehensive Cancer Center

Episode Notes

Since arriving at Feinberg in 2004, Scholtens has played a central role in the dramatic expansion of biostatistics at the medical school. Now the Director of NUDACC, Scholtens brings her expertise and leadership to large-scale, multicenter studies that can lead to clinical and public health practice decision-making.

After discovering her love of statistics as a high school math teacher, Scholtens studied bioinformatics in a PhD program before arriving at Feinberg in 2004.
Feinberg’s commitment to biostatistics has grown substantially in recent decades. Scholtens was only one of five biostatisticians when she arrived. Now she is part of a division with almost 50 people.
She says being a good biostatistician requires curiosity about other people’s work, knowing what questions to ask and tenacity to understand subtitles of so much data.
At NUDACC, Scholtens and her colleagues specialize in large-scale, multicenter prospective studies and clinical trials that lead to clinical or public health practice decision-making. They operate at the executive level and oversee all aspects of the study design.
Currently, Scholtens is involved with the launch of a large study, along with The Ohio State University, that received a $14 million grant to look at the effectiveness of aspirin in the prevention of hypertensive disorders in pregnancy.
Scholtens first started her work in data coordinating through the Hyperglycemia Adverse Pregnancy Outcome (HAPO) study, which looked at 25,000 pregnant individuals. This led to a continued interest in fetal and maternal health.
When it comes to supportive working environments, Scholtens celebrates the culture at Feinberg, and especially her division in biostatistics, for being collaborative as well as genuinely supportive of each other’s projects. She attributes this to strong leadership which established a culture with these guiding principles.

Additional Reading

Read more about the ASPIRIN trial and other projects taking place at NUDACC
Discover a study linking mothers’ obesity-related genes to babies’ birth weight, which Scholtens worked in through the HAPO study
Browse all of Scholtens recent publications

Recorded on February 21, 2024.

Continuing Medical Education Credit

Physicians who listen to this podcast may claim continuing medical education credit after listening to an episode of this program..

Target Audience

Academic/Research, Multiple specialties

Learning Objectives

At the conclusion of this activity, participants will be able to:

Identify the research interests and initiatives of Feinberg faculty.
Discuss new updates in clinical and translational research.

Accreditation Statement

The Northwestern University Feinberg School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.

Credit Designation Statement

The Northwestern University Feinberg School of Medicine designates this Enduring Material for a maximum of 0.50 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

American Board of Surgery Continuous Certification Program

Successful completion of this CME activity enables the learner to earn credit toward the CME requirement(s) of the American Board of Surgery’s Continuous Certification program. It is the CME activity provider's responsibility to submit learner completion information to ACCME for the purpose of granting ABS credit.

All the relevant financial relationships for these individuals have been mitigated.

Disclosure Statement

Denise Scholtens, PhD, has nothing to disclose. Course director, Robert Rosa, MD, has nothing to disclose. Planning committee member, Erin Spain, has nothing to disclose. FSM’s CME Leadership, Review Committee, and Staff have no relevant financial relationships with ineligible companies to disclose.

Read the Full Transcript

[00:00:00] Erin Spain, MS: This is Breakthroughs, a podcast from Northwestern University Feinberg School of Medicine. I'm Erin Spain, host of the show. Northwestern University Feinberg School of Medicine is home to a team of premier faculty and staff biostatisticians, who are the driving force of data analytic innovation and excellence here. Today, we are talking with Dr. Denise Scholtens, a leader in biostatistics at Northwestern, about the growing importance of the field, and how she leverages her skills to collaborate on several projects in Maternal and Fetal Health. She is the Director of the Northwestern University Data Analysis and Coordinating Center, NUDACC, and Chief of Biostatistics in the Department of Preventive Medicine, as well as Professor of Preventive Medicine and Neurological Surgery. Welcome to the show.

[00:01:02] Denise Scholtens, PhD: Thank you so much.

[00:01:02] Erin Spain, MS: So you have said in the past that you were drawn to this field of biostatistics because you're interested in both math and medicine, but not interested in becoming a clinician. Tell me about your path into the field and to Northwestern.

[00:01:17] Denise Scholtens, PhD: You're right. I have always been interested in both math and medicine. I knew I did not want to be involved in clinical care. Originally, fresh out of college, I was a math major and I taught high school math for a couple of years. I really enjoyed that, loved the kids, loved the teaching parts of things. Interestingly enough, my department chair at the time assigned me to teach probability and statistics to high school seniors. I had never taken a statistics course before, so I was about a week ahead of them in our classes and found that I just really enjoyed the discipline. So as much as I loved teaching, I did decide to go ahead and invest in this particular new area that I had found and I really enjoyed. So I wanted to figure out how I could engage in the field of statistics. Decided to see, you know, exactly how studying statistics could be applied to medicine. At the time, Google was brand new. So I literally typed in the two words math and medicine to see what would come up. And the discipline of biostatistics is what Google generated. And so here I am, I applied to grad school and it's been a great fit for me.

[00:02:23] Erin Spain, MS: Oh, that's fantastic. So you went on to get a PhD, and then you came to Northwestern in 2004. And so tell me a little bit about the field then and how it's changed so dramatically since.

[00:02:36] Denise Scholtens, PhD: So yes, I started here at Northwestern in 2004, just a few months after I had defended my thesis. At the time there was really an emerging field of study called bioinformatics. So I wrote my thesis in the space of genomics data analysis with what at the time was a brand new technology, microarrays. This was the first way we could measure gene transcription at a high throughput level. So I did my thesis work in that space. I studied at an institution with a lot of strengths and very classical statistics. So things that we think of in biostatistics like clinical trial design, observational study analysis, things like that. So I had really classic biostatistics training and then complimented that with sort of these emerging methods with these high dimensional data types. So I came to Northwestern here and I sort of felt like I lived in two worlds. I had sort of classic biostat clinical trials, which were certainly, you know, happening here. And, that work was thriving here at Northwestern, but I had this kind of new skillset, and I just didn't quite know how to bring the two together. That was obviously a long time ago, 20 years ago. Now we think of personalized medicine and genomic indicators for treatment and, you know, there's a whole variety of omics data variations on the theme that are closely integrated with clinical and population level health research. So there's no longer any confusion for me about how those two things come together. You know, they're two disciplines that very nicely complement each other. But yeah, I think that does speak to how the field has changed, you know, these sort of classic biostatistics methods are really nicely blended with a lot of high dimensional data types. And it's been fun to be a part of that.

[00:04:17] Erin Spain, MS: There were only a handful of folks like you at Northwestern at the time. Tell me about now and the demand for folks with your skill set.

[00:04:26] Denise Scholtens, PhD: When I came to Northwestern, I was one of a very small handful of biostatistics faculty. There were five of us. We were not even called a division of biostatistics. We were just here as the Department of Preventive Medicine. And a lot of the work we did was really very tightly integrated with the epidemiologists here in our department and we still do a lot of that for sure. There was also some work going on with the Cancer Center here at Northwestern. But yeah, a pretty small group of us, who has sort of a selected set of collaborations. You know, I contrast that now to our current division of biostatistics where we are over 20s, pushing 25, depending on exactly how you want to count. Hoping to bring a couple of new faculty on board this calendar year. We have a staff of about 25 statistical analysts. And database managers and programmers. So you know, when I came there were five faculty members and I think two master's level staff. We are now pushing, you know, pushing 50 people in our division here so it's a really thriving group.

[00:05:26] Erin Spain, MS: in your opinion, what makes a good biostatistician? Do you have to have a little bit of a tough skin to be in this field?

Denise Scholtens, PhD: I do think it's a unique person who wants to be a biostatistician. There are a variety of traits that can lead to success in this space. First of all, I think it's helpful to be wildly curious about somebody else's work. To be an excellent collaborative biostatistician, you have to be able to learn the language of another discipline. So some other clinical specialty or public health application. Another trait that makes a biostatistician successful is to be able to ask the right questions about data that will be collected or already have been collected. So understanding the subtleties there, the study design components that lead to why we have the data that we have. You know, a lot of our data, you could think of it in a simple flat file, right? Like a Microsoft Excel file with rows and columns. That certainly happens a lot, but there are a lot of incredibly innovative data types out there: wearables technology, imaging data, all kinds of high dimensional data. So I think a tenacity to understand all of the subtleties of those data and to be able to ask the right questions. And then I think for a biostatistician at a medical school like ours, being able to blend those two things, so understanding what the data are and what you have to work with and what you're heading toward, but then also facilitating the translation of those analytic findings for the audience that really wants to understand them. So for the clinicians, for the patients, for participants and the population that the findings would apply to.

Erin Spain, MS: It must feel good, though, in those situations where you are able to help uncover something to improve a study or a trial.

[00:07:07] Denise Scholtens, PhD: It really does. This is a job that's easy to get out of bed for in the morning. There's a lot of really good things that happen here. It's exciting to know that the work we do could impact clinical practice, could impact public health practice. I think in any job, you know, you can sometimes get bogged down by the amount of work or the difficulty of the work or the back and forth with team members. There's just sort of all of the day to day grind, but to be able to take a step back and remember the actual people who are affected by our own little niche in this world. It's an incredibly helpful and motivating practice that I often keep to remember exactly why I'm doing what I'm doing and who I'm doing it for.

[00:07:50] Erin Spain, MS: Well, and another important part of your work is that you are a leader. You are leading the center, NUDACC, that you mentioned, Northwestern University Data Analysis and Coordinating Center. Now, this has been open for about five years. Tell me about the center and why it's so crucial to the future of the field.

[00:08:08] Denise Scholtens, PhD: We specialize at NUDACC in large scale, multicenter prospective studies. So these are the clinical trials or the observational studies that often, most conclusively, lead to clinical or public health practice decision making. We focus specifically on multicenter work. Because it requires a lot of central coordination and we've specifically built up our NUDACC capacity to handle these multi center investigations where we have a centralized database, we have centralized and streamlined data quality assurance pipelines. We can help with central team leadership and organization for large scale networks. So we have specifically focused on those areas. There's a whole lot of project management and regulatory expertise that we have to complement our data analytics strengths as well. I think my favorite part of participating in these studies is we get involved at the very beginning. We are involved in executive level planning of these studies. We oversee all components of study design. We are intimately involved in the development of the data capture systems. And in the QA of it. We do all of this work on the front end so that we get all of the fun at the end with the statistics and can analyze data that we know are scientifically sound, are well collected, and can lead to, you know, really helpful scientific conclusions.

[00:09:33] Erin Spain, MS: Tell me about that synergy between the clinicians and the other investigators that you're working with on these projects.

[00:09:41] Denise Scholtens, PhD: It is always exciting, often entertaining. Huge range of scientific opinion and expertise and points of view, all of which are very valid and very well informed. All of the discussion that could go into designing and launching a study, it's just phenomenally interesting and trying to navigate all of that and help bring teams to consensus in terms of what is scientifically most relevant, what's going to be most impactful, what is possible given the logistical strengths. Taking all of these well informed, valid, scientific points of view and being a part of the team that helps integrate them all toward a cohesive study design and a well executed study. That's a unique part of the challenge that we face here at NUDACC, but an incredibly rewarding one. It's also such an honor and a gift to be able to work with such a uniformly gifted set of individuals. Just the clinical researchers who devote themselves to these kinds of studies are incredibly generous, incredibly thoughtful and have such care for their patients and the individuals that they serve, that to be able to sit with them and think about the next steps for a great study is a really unique privilege.

[00:10:51] Erin Spain, MS: How unique is a center like this at a medical school?

[00:10:55] Denise Scholtens, PhD: It's fairly unique to have a center like this at a medical school. Most of the premier medical research institutions do have some level of data coordinating center capacity. We're certainly working toward trying to be one of the nation's best, absolutely, and build up our capacity for doing so. I'm actually currently a part of a group of data coordinating centers where it's sort of a grassroots effort right now to organize ourselves and come up with, you know, some unified statements around the gaps that we see in our work, the challenges that we face strategizing together to improve our own work and to potentially contribute to each other's work. I think maybe the early beginnings of a new professional organization for data coordinating centers. We have a meeting coming up of about, I think it's 12 to 15 different institutions, academic research institutions, specifically medical schools that have centers like ours to try to talk through our common pain points and also celebrate our common victories.

[00:11:51] Erin Spain, MS: I want to shift gears a little bit to talk about some of your research collaborations, many of which focus on maternal and fetal health and pregnancy. You're now involved with a study with folks at the Ohio State University that received a 14 million grant looking at the effectiveness of aspirin in the prevention of hypertensive disorders in pregnancy. Tell me about this work.

[00:12:14] Denise Scholtens, PhD: Yes, this is called the aspirin study. I suppose not a very creative name, but a very appropriate one. What we'll be doing in this study is looking at two different doses of aspirin for trying to prevent maternal hypertensive disorders of pregnancy in women who are considered at high risk for these disorders. This is a huge study. Our goal is to enroll 10,742 participants. This will take place at 11 different centers across the nation. And yes, we at NUDACC will serve as the data coordinating center here, and we are partnering with the Ohio State University who will house the clinical coordinating center. So this study is designed to look at two different doses to see which is more effective at preventing hypertensive disorders of pregnancy. So that would include gestational hypertension and preeclampsia. What's really unique about this study and the reason that it is so large is that it is specifically funded to look at what's called a heterogeneity of treatment effect. What that is is a difference in the effectiveness of aspirin in preventing maternal hypertensive disorders, according to different subgroups of women. We'll specifically have sufficient statistical power to test for differences in treatment effectiveness. And we have some high priority subgroups that we'll be looking at. One is a self-identified race. There's been a noted disparity in maternal hypertensive disorders, for individuals who self identify according to different races. And so we will be powered to see if aspirin has comparable effectiveness and hopefully even better effectiveness for the groups who really need it, to bring those rates closer to equity which is, you know, certainly something we would very strongly desire to see. We'll also be able to look at subgroups of women according to obesity, according to maternal age at pregnancy, according to the start time of aspirin when aspirin use is initiated during pregnancy. So that's why the trial is so huge. For a statistician, the statisticians out there who might be listening, this is powered on a statistical interaction term, which doesn't happen very often. So it's exciting that the trial is funded in that way.

[00:14:27] Erin Spain, MS: Tell me a little bit more about this and how your specific skills are going to be utilized in this study.

[00:14:32] Denise Scholtens, PhD: Well, there are three biostatistics faculty here at Northwestern involved in this. So we're definitely dividing and conquering. Right now, we're planning this study and starting to stand it up. So we're developing our statistical analysis plans. We're developing the database. We are developing our randomization modules. So this is the piece of the study where participants are randomized to which dose of aspirin they're going to receive. Because of all of the subgroups that we're planning to study, we need to make especially sure that the assignments of which dose of aspirin are balanced within and across all of those subgroups. So we're going to be using some adaptive randomization techniques to ensure that that balance is there. So there's some fun statistical and computer programming innovation that will be applied to accomplish those things. So right now, there are usually two phases of a study that are really busy for us. That's starting to study up and that's where we are. And so yes, it is very busy for us right now. And then at the end, you know, in five years or so, once recruitment is over, then we analyze all the data,

[00:15:36] Erin Spain, MS: Are there any guidelines out there right now about the use of aspirin in pregnancy. What do you hope that this could accomplish?

Prescribing aspirin use for the prevention of hypertension during pregnancy is not uncommon at all. That is actually fairly routinely done, but that it's not outcomes based in terms of which dosage is most effective. So 81 milligrams versus 162 milligrams. That's what we will be evaluating. And my understanding is that clinicians prescribe whatever they think is better, and I'm sure those opinions are very well informed but there is very little outcome based evidence for this in this particular population that we'll be studying. So that would be the goal here, would be to hopefully very conclusively say, depending on the rates of the hypertensive disorders that we see in our study, which of the two doses of aspirin is more effective. Importantly, we will also be tracking any side effects of taking aspirin. And so that's also very much often a part of the evaluation of You know, taking a, taking a drug, right, is how safe is it? So we'll be tracking that very closely as well. Another unique part of this study is that we will be looking at factors that help explain aspirin adherence. So we are going to recommend that participants take their dose of aspirin daily. We don't necessarily expect that's always going to happen, so we are going to measure how much of their prescribed dose they are actually taking and then look at, you know, factors that contribute to that. So be they, you know, social determinants of health or a variety of other things that we'll investigate to try to understand aspirin adherence, and then also model the way in which that adherence could have affected outcomes.

Erin Spain, MS: This is not the first study that you've worked on involving maternal and fetal health. Tell me about your interest in this particular area, this particular field, and some of the other work that you've done.

[00:17:31] Denise Scholtens, PhD: So I actually first got my start in data coordinating work through the HAPO study. HAPO stands for Hyperglycemia Adverse Pregnancy Outcome. That study was started here at Northwestern before I arrived. Actually recruitment to the study occurred between 2000 and 2006. Northwestern served as the central coordinating center for that study. It was an international study of 25,000 pregnant individuals who were recruited and then outcomes were evaluated both in moms and newborns. When I was about mid career here, all the babies that were born as a part of HAPO were early teenagers. And so we conducted a follow up study on the HAPO cohort. So that's really when I got involved. It was my first introduction to being a part of a coordinating center. As I got into it, though, I saw the beauty of digging into all of these details for a huge study like this and then saw these incredible resources that were accumulated through the conduct of such a large study. So the data from the study itself is, was of course, a huge resource. But then also we have all of these different samples that sit in a biorepository, right? So like usually blood sample collection is a big part of a study like this. So all these really fun ancillary studies could spin off of the HAPO study. So we did some genomics work. We did some metabolomics work. We've integrated the two and what's called integrated omics. So, you know, my work in this space really started in the HAPO study. And I have tremendously enjoyed integrating these high dimensional data types that have come from these really rich data resources that have all, you know, resulted because of this huge multicenter longitudinal study. So I kind of accidentally fell into the space of maternal and fetal health, to be honest. But I just became phenomenally interested in it and it's been a great place.

[00:19:24] Erin Spain, MS: Would you say that this is also a population that hasn't always been studied very much in biomedical science?

[00:19:32] Denise Scholtens, PhD: I think that that is true, for sure. There are some unique vulnerabilities, right, for a pregnant individual and for the fetus, right, and in that situation. You know, the vast majority of what we do is really only pertaining to the pregnant participant but, you know, there are certainly fetal outcomes, newborn outcomes. And so, I think conducting research in this particular population is a unique opportunity and there are components of it that need to be treated with special care given sort of this unique phase of human development and this unique phase of life.

[00:20:03] Erin Spain, MS: So, as data generation just really continues to explode, and technology is advancing so fast, faster than ever, where do you see this field evolving, the field of biostatistics, where do you see it going in the next five to ten years?

[00:20:19] Denise Scholtens, PhD: That's a great question. I think all I can really tell you is that I'm continually surprised by new data types. I think that we will see an emergence of a whole new kind of technology that we probably can't even envision five years from now. And I think that the fun part about being a biostatistician is seeing what's happening and then trying to wrap your mind around the possibilities and the actual nature of the data that are collected. You know, I think back to 2004 and this whole high throughput space just felt so big. You know, we could look at gene transcription across the genome using one technology. And we could only look at one dimension of it. Right now it just seems so basic. When I think about where the field has come over the past 20 years, it's just phenomenal. I think we're seeing a similar emergence of the scale and the type of data in the imaging space and in the wearable space, with EHR data, just. You know, all these different technologies for capturing, capturing things that we just never even conceived of before. I do hope that we continue to emphasize making meaningful and translatable conclusions from these data. So actionable conclusions that can impact the way that we care for others around us. I do hope that remains a guiding principle in all that we do.

[00:21:39] Erin Spain, MS: Why is Northwestern Medicine and Northwestern Feinberg School of Medicine such a supportive environment to pursue this type of work?

[00:21:47] Denise Scholtens, PhD: That's a wonderful question and one, honestly, that faculty candidates often ask me. When we bring faculty candidates in to visit here at Northwestern, they immediately pick up on the fact that we are a collaborative group of individuals who are for each other. Who want to see each other succeed, who are happy to share the things that we know and support each other's work, and support each other's research, and help strategize around the things that we want to accomplish. There is a strong culture here, at least in my department and in my division that I've really loved that continues to persist around really genuinely collaborating and genuinely sharing lessons learned and genuinely supporting each other as we move toward common goals. We've had some really strong, generous leadership who has helped us to get there and has helped create a culture where those are the guiding principles. In my leadership role is certainly something that I strive to maintain. Really hope that's true. I'm sure I don't do it perfectly but that's absolutely something I want to see accomplished here in the division and in NUDACC for sure.

[00:22:50] Erin Spain, MS: Well, thank you so much for coming on the show and telling us about your path here to Northwestern and all of the exciting work that we can look forward to in the coming years.

[00:22:59] Denise Scholtens, PhD: Thank you so much for having me. I've really enjoyed this.

[00:23:01] Erin Spain, MS: You can listen to shows from the Northwestern Medicine Podcast Network to hear more about the latest developments in medical research, health care, and medical education. Leaders from across specialties speak to topics ranging from basic science to global health to simulation education. Learn more at feinberg. northwestern.edu/podcasts.

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A Biostatistics PhD Application Notebook [with Statement of Purpose]
There's a lot of opinions surrounding the statement of purpose for Biostatistics PhDs, from, "it's very important and the only way to set yourself apart to the application committee," to, "nobody reads it and it won't affect your application.". I opted to believe the first set of opinions and took my SOP seriously.
Application
If you are in the process of completing a prerequisite, make sure that the transcript you upload indicates this (or mention it in your statement of purpose). Statement of Purpose. Submit a 1-2 page statement which includes your reasons for pursuing graduate studies in the field of biostatistics and at our program specifically, your area(s) of ...
Rollins School of Public Health
The PhD program in biostatistics (BIOS) is offered through Emory's Laney Graduate School.The program is designed for people with strong quantitative skills and a background or interest in the biological, medical, or health sciences. The program is ideal for students seeking to deepen their knowledge of biostatistics through advanced course work, research, analysis, and collaboration.
Doctor of Philosophy (PhD) Biostatistics
7. Upload your personal statement. Statement of purpose that should include why and or how you became interested in Biostatistics. It would also be helpful to discuss research and work experience relevant to your proposed plan of study. 8. Upload your resume or CV. 9. Enter names of recommenders.
Prospective Students
Statement of Purpose (details provided below) Transcripts (official transcripts will be required if admitted into the program); GPA >= 3.0 required for admission. ... PhD in Biostatistics. The PhD program is distinguished by its high mathematical content and rigorous technical training in theory and methods, as well as, training in practical ...
Doctoral Program
PhD in Biostatistics. The PhD program is designed for those who have demonstrated both interest and ability in scholarly research. The department's program is designed to prepare students for careers in the theory and practice of biostatistics and bioinformatics, and includes training in the development of methodology, consulting, teaching, and collaboration on a broad spectrum of problems ...
Ph.D. in Biostatistics Admissions
Statement of purpose; Graduate Record Examination (GRE) scores are not required for students applying to the PhD in Biostatistics Program for Fall 2023 matriculation. Additional information about the Duke University Graduate School application may be found at the Graduate School application website.
Apply for a PhD in Biostatistics
Email: [email protected]. Telephone: 734-615-9817. Department of Biostatistics. School of Public Health. University of Michigan. 1415 Washington Heights. Ann Arbor, MI 48109-2029. Fax: 734-763-2215. Apply for a PhD in Biostatistics | University of Michigan School of Public Health.
PhD in Biostatistics
FUNDING. All students admitted to the PhD in biostatistics program, including international students, are guaranteed full funding, which includes a stipend as well as tuition and health insurance for four years, provided they make satisfactory progress. In practice, many students require a fifth year to complete the doctoral program, and ...
Biostatistics PhD Program
While many of the applicants admitted to Columbia's PhD program in biostatistics have already completed (or are completing) master's degrees in biostatistics, statistics, or a related field, admission is open to well qualified students holding (or completing) bachelor's degrees. ... a statement of academic purpose, and three letters of ...
Biostatistics
The Biostatistics (BIOSTATS) area of study is focused heavily on research to address challenges in public health, biomedical research, and computational biology. BIOSTATS students receive rigorous training in statistical theory and methods, as well as in computation. Biostatisticians play a unique role in safeguarding public health and ...
Application Process
CV: Current curriculum vitae: Statement of Purpose: In an essay, tell us why you want to study biostatistics. If you like, please include a short description of how your personal background contributes to Vanderbilt University's mission of equity, diversity, and inclusion.The essay should be 1-2 pages; the font must be 11 point or larger.
Pre-Application Review Service
Current Biostatistics PhD students will review and provide feedback on a prospective applicant's resume or curriculum vitae (CV), personal statement and statement of purpose. The process is open to students who are applying to the PhD program in the University of Washington Department of Biostatistics.
Biostatistics PhD Program
The PhD program in biostatistics is designed for individuals with strong quantitative skills and background or interest in the biological, medical, or health sciences. To the extent possible, the curriculum of each student is tailored to his or her background and interests. Students can enter the PhD program with a bachelor's or a master's degree.
Biostatistics
The Ph.D. program is administered by an active, expanding and highly interdisciplinary faculty in the Department of Biostatistics. Major areas of research activity include Bayesian inference, analysis of biomarkers and diagnostic tests, causal inference and missing data, time series and functional data analysis, modeling of social networks, bioinformatics, longitudinal data, and multilevel ...
PhD in Biostatistics
Description The doctoral program in Biostatistics trains future leaders, highly qualified as independent investigators and teachers, and who are well-trained practitioners of biostatistics. The program includes coursework in biostatistics, statistics, and one or more public health or biomedical fields. In addition, successful candidates are required to pass PhD applied and theory exams and ...
MS Thesis to PhD Admissions
The applicant submits a revised Statement of Purpose and transcripts from previous institutions, and arranges for one Letter of Recommendation to be submitted from a Biostatistics faculty member. This material is sent to [email protected] . The MS-to-PhD application deadline is provided to eligible students in their progress review letter.
Biostatistics PhD
The PhD student-to-faculty ratio is approximately 1.5:1, one of the lowest of any biostatistics program in the nation. Impact. The Division of Biostatistics & Health Data Science (BHDS) plays a leadership role in many national and international clinical trials, including the first vaccine trial for Ebola and the largest HIV/AIDS treatment trial ...
Guidelines for Writing Statement of Objectives
1. Read the instructions for the written statement carefully and follow them. Do not exceed the 2-page limit: do not shrink the font size to fit the 2-page limit. Eleven or twelve point font and two pages is sufficient for meeting the requirements of the essay. 2.
Biostatistics Personal Statement
Below are some of the essential tips for writing a biostatistics personal statement. Discuss Your Long-Term Objectives And Desires. When writing a Biostatistics Personal Statement, this is among the most significant elements to consider. On the pitch, sharing your dreams, hopes, and future goals is a terrific approach to increase your chances.
Statement of Purpose
The Statement of Purpose and Objectives should not exceed 600 words, although SOPHAS allows for a higher word count. In your statement, please describe the following: Academic and/or professional preparation for a career in public health. Your focused interest in the degree program/department or MPH field of study to which you are applying.
Statement Of Purpose For Biostatistics
912 Words. 4 Pages. Open Document. Statement of Purpose for the Biostatistics Graduate Program at. Johns Hopkins University. Yudi Zhang. Growing up in a family of doctors influenced me to pursue the knowledge of health and diseases. I long for devoting myself to public health. With data explosion, I realized that data-driven science is ...
Biostatistics Faculty & Research Staff
Faculty and research staff in the Dept of Biostatistics collaborate with clinical researchers across the medical school, Yale New Haven Hospital and the Veterans Administration in collaboration the Yale Center for Clinical Investigation and faculty research labs.YSPH Biostatistics comprehensively integrates design, data management and analysis into Yale's clinical research to maximize ...
Driving Innovations in Biostatistics with Denise Scholtens, PhD
It is the CME activity provider's responsibility to submit learner completion information to ACCME for the purpose of granting ABS credit. All the relevant financial relationships for these individuals have been mitigated. Disclosure Statement. Denise Scholtens, PhD, has nothing to disclose. Course director, Robert Rosa, MD, has nothing to ...

A Biostatistics PhD Application Notebook [with Statement of Purpose]

Background and FAQs

Do you need a PhD to be successful in biostatistics?

Should you take time off before starting your PhD?

The Application Process

Assessing which schools to apply to

Application organization

Application Components

Letters of Recommendation

Curriculum Vitae

The Statement of Purpose (SOP)

The Personal Statement

Application Fees

Pre Application Review Service (PARS)

Interviews and visit days

Decision time

Miscellaneous notes

Admission rates

Reaching out to professors in advance

Looking ahead, preparing for my PhD coursework

Graduate Programs in Biostatistics

Admissions Overview

New for Fall 2024 Master's Application!

Statement of Purpose

Application Deadline

Application Fee

PhD in Biostatistics

PhD Admission Requirements

Apply for a PhD in Biostatistics

Those applying for the Doctor of Philosophy program should complete the following:

Minimum Requirements

Current online grades policy

Frequently Asked Questions

Information For

Student Resources

Departments

Our Faculty

Become a Student

Doctoral Programs

Admissions Requirements

Biostatistics Graduate Program

Graduate Programs

Additional Resources

Application Information

Application Requirements

GRE General:

Personal Statement:

Additional Requirements:

Dates/Deadlines

Alumni Careers

Contact and Location

PhD in Biostatistics

Likely Careers

Competencies

UW NetID sign-in

UW NetID Help

School of Public Health

Biostatistics PhD

Biostatistics PhD Profiles

Advantages of the Program

Connect with Us

Biostatistics Personal Statement

How To Write A Statement Of Purpose For Biostatistics Masters?

Tips To Write A Biostatistics Personal Statement

Biostatistics/Bioinformatics Personal Statement Examples

Final Thoughts: Biostatistics Personal Statement

Related Queries

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Statement of Purpose

Statement requirement for all Master of Science (SM) and MPH (excluding JD/MPH):

Statement requirement for JD/MPH:

Statement requirement for DrPH:

Statement requirement for MHCM:

Clinical Trials and Outcomes

Faculty of Interest

Centers and other resources

Driving Innovations in Biostatistics with Denise Scholtens, PhD

Episode Notes

Continuing Medical Education Credit