university of toronto phd biostatistics

School of Graduate Studies

Program overview.

Statistical Sciences involves the study of random phenomena and encompasses a broad range of scientific, industrial, and social processes. As data become ubiquitous and easier to acquire, particularly on a massive scale, models for data are becoming increasingly complex. The past several decades have witnessed a vast impact of statistical methods on virtually every branch of knowledge and empirical investigation.

​There are also opportunities for study and research in the fields of (a)  Statistical Theory and Applications  and (b)  Probability , leading to the  Master of Science  and  Doctor of Philosophy  degrees, and (c)  Actuarial Science and Mathematical Finance , leading to the  Doctor of Philosophy  degree. Please visit the  departmental website  for further details about the fields offered, the research being conducted, and the course offerings.

​ The department has substantial computing facilities available and operates a statistical consulting service for the University’s research community. Programs of study may involve association with other departments such as Astronomy & Astrophysics, Computer Science, Economics, Engineering, Environment, Information, Management, Mathematics, Philosophy, Psychology, Public Health, and Sociology. The department maintains an active seminar series and strongly encourages graduate student participation.

PhD applicants will be able to select up to three potential supervisors at the time of their applications. Supervisors are then matched and assigned by the department upon acceptance of offer to the PhD program based on research areas of interest.

Quick Facts

Master of science, program description.

Students in the MSc program can conduct research in the fields of 1) Statistical Theory and Applications or 2) Probability. The program offers numerous courses in theoretical and applied aspects of Statistical Sciences, which prepare students for pursuing a PhD program or directly entering the data science workforce.

The MSc program can be taken on a full-time or part-time basis. Program requirements are the same for the full-time and part-time options.

Fields: 1) Statistical Theory and Applications; 2) Probability

Minimum admission requirements.

Admission to the MSc program is competitive, and applicants are admitted under the General Regulations of the School of Graduate Studies. Admission requirements for the Statistical Theory and Applications field and the Probability field are identical. Successful applicants have:

An appropriate bachelor's degree from a recognized university in a related field such as statistics, actuarial science, mathematics, economics, engineering, or any discipline where there is a significant quantitative component. Studies must include significant exposure to statistics, computer science, and mathematics, including coursework in advanced calculus, computational methods, linear algebra, probability, and statistics.

An average grade equivalent to at least a University of Toronto mid-B in the final year or over senior courses.

Three letters of reference.

A curriculum vitae.

Applicants whose primary language is not English and who graduated from a university where the language of instruction and examination was not English must demonstrate proficiency in English. See General Regulations section 4.3 for requirements.

Program Requirements

Both the Statistical Theory and Applications field and the Probability field have the same program requirements. All programs must be approved by the Associate Chair for Graduate Studies.

Students must complete a total of 4.0 full-course equivalents (FCEs), of which 2.0 must be chosen from the list below:

STA2101H Methods of Applied Statistics I

STA2201H Methods of Applied Statistics II

STA2111H Probability Theory I

STA2211H Probability Theory II

STA2112H Mathematical Statistics I

STA2212H Mathematical Statistics II

The remaining 2.0 FCEs may be selected from:

Any Department of Statistical Sciences 2000-level course or higher.

Any 1000-level course or higher in another graduate unit at the University of Toronto with sufficient statistical, computational, probabilistic, or mathematical content.

One 0.5 FCE as a reading course.

One 0.5 FCE as a research project.

A maximum of 1.0 FCE from any STA 4500-level modular course (each are 0.25 FCE).

All programs must be approved by the Associate Chair for Graduate Studies. Students must meet with the Associate Chair to ensure that their program meets the requirements and is of sufficient depth.

Part-time students are limited to taking 1.0 FCE during each session. In exceptional cases, the Associate Chair for Graduate Studies may approve 1.5 FCEs in a given session.

Program Length

3 sessions full-time (typical registration sequence: F/W/S); 6 sessions part-time

3 years full-time; 6 years part-time

Doctor of Philosophy

Students in the PhD program can conduct research in the fields of 1) Statistical Theory and Applications or 2) Probability or 3) Actuarial Science and Mathematical Finance. The research conducted in the department is vast and covers a diverse set of areas in theoretical and applied aspects of Statistical Sciences. Students have the opportunity to work in multidisciplinary areas and team up with researchers in, for example, Biostatistics, Computer Science, Economics, Engineering, and the Rotman School of Management. The main purpose of the program is to prepare students for pursuing advanced research both in academia and in research institutes.

Applicants may enter the PhD program via one of two routes: 1) following completion of an appropriate master’s degree or 2) direct entry after completing an appropriate bachelor’s degree (excluding Actuarial Science and Mathematical Finance).

PhD Program

Admission to the PhD program is competitive, and applicants are admitted under the General Regulations of the School of Graduate Studies.

Applicants may be accepted with a master's degree in statistics from a recognized university with at least a B+ average. Applicants with degrees in biostatistics, computer science, economics, engineering, mathematics, physics, or any discipline where there is a significant quantitative component will also be considered.

Three letters of recommendation.

A letter of intent or personal statement outlining goals for graduate studies.

Course Requirements

During Year 1, students must successfully complete a total of 3.0 full-course equivalents (FCEs) as follows:

STA3000Y Advanced Theory of Statistics (1.0 FCE)

     and two of the following:

STA2101H Methods of Applied Statistics I and STA2201H Methods of Applied Statistics II (1.0 FCE)

STA2111H Probability Theory I and STA2211H Probability Theory II (1.0 FCE)

STA2311H Advanced Computational Methods for Statistics I and STA2312H Advanced Computational Methods for Statistics II (1.0 FCE).

Courses must be chosen in consultation with the advisor and approved by the Associate Chair of Graduate Studies.

Comprehensive Examination Requirements

Within Years 1 and 2, students must complete a two-part comprehensive examination: 1) an in-class written comprehensive exam and 2) a research comprehensive exam.

Students must attempt the in-class written comprehensive by the end of Year 1. If a student fails this portion of the comprehensive exam, one further attempt will be allowed by the end of Year 2. Students who achieve A or A+ grades in all required coursework are exempt from the in-class written exam.

Students must attempt the research comprehensive exam by the beginning of Year 2, which includes a technical report and an oral presentation. If a student fails this portion of the comprehensive exam, one further attempt will be allowed at the end of Year 2.

Students must pass both the in-class written exam and the research exam to continue in the program.

Thesis Requirements

Conducting original research is the most important part of doctoral work. The thesis document must constitute significant and original contribution to the field. Students will have yearly meetings with a committee of no less than three faculty members to assess their progress. The completed thesis must be presented and defended within the Department of Statistical Sciences in addition to being presented and defended at the School of Graduate Studies.

Residency Requirements

Students must also satisfy a two-year residency requirement, whereby students must be on campus full-time and consequently in geographical proximity to be able to participate fully in the University activities associated with the program.

PhD Program (Direct-Entry)

Applicants may be accepted via direct entry with a bachelor's degree in statistics from a recognized university with at least an A– average. The department also encourages applicants from biostatistics, computer science, economics, engineering, mathematics, physics, or any discipline where there is a significant quantitative component.

Students must successfully complete a total of 5.0 full-course equivalents (FCEs) as follows:

Year 1: complete 3.0 FCEs:

STA3000Y Advanced Theory of Statistics (1.0 FCE) and two of the following:

Complete an additional 2.0 FCEs at the graduate level. The additional courses must be approved by the Associate Chair of Graduate Studies.

Students must also satisfy a three-year residency requirement, whereby students must be on campus full-time and consequently in geographical proximity to be able to participate fully in the University activities associated with the program.

Field: Actuarial Science and Mathematical Finance

During Year 1, students must complete the following 3.0 full-course equivalents (FCEs) :

(1.5 FCEs) All of:

STA2111H Probability Theory I ,

STA2211H Probability Theory II , and

STA2503H Applied Probability for Mathematical Finance .

(0.5 FCE) One of:

STA2501H Advanced Topics in Actuarial Science or

STA4246H Research Topics in Mathematical Finance .

(1.0 FCE) One of:

STA2101H Methods of Applied Statistics I and STA2201H Methods of Applied Statistics II or

STA2311H Advanced Computational Methods for Statistics I and STA2312H Advanced Computational Methods for Statistics II or

STA3000Y Advanced Theory of Statistics .

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Statistical sciences: statistics phd, doctor of philosophy, program description.

Students in the PhD program can conduct research in the fields of 1) Statistical Theory and Applications or 2) Probability or 3) Actuarial Science and Mathematical Finance. The research conducted in the department is vast and covers a diverse set of areas in theoretical and applied aspects of Statistical Sciences. Students have the opportunity to work in multidisciplinary areas and team up with researchers in, for example, Biostatistics, Computer Science, Economics, Engineering, and the Rotman School of Management. The main purpose of the program is to prepare students for pursuing advanced research both in academia and in research institutes.

Applicants may enter the PhD program via one of two routes: 1) following completion of an appropriate master’s degree or 2) direct entry after completing an appropriate bachelor’s degree (excluding Actuarial Science and Mathematical Finance).

Fields: 1) Statistical Theory and Applications; 2) Probability

Phd program, minimum admission requirements.

Admission to the PhD program is competitive, and applicants are admitted under the General Regulations of the School of Graduate Studies.

Applicants may be accepted with a master's degree in statistics from a recognized university with at least a B+ average. Applicants with degrees in biostatistics, computer science, economics, engineering, mathematics, physics, or any discipline where there is a significant quantitative component will also be considered.

Three letters of recommendation.

A curriculum vitae.

A letter of intent or personal statement outlining goals for graduate studies.

Applicants whose primary language is not English and who graduated from a university where the language of instruction and examination was not English must demonstrate proficiency in English. See General Regulations section 4.3 for requirements.

Program Requirements

Course requirements.

During Year 1, students must successfully complete a total of 3.0 full-course equivalents (FCEs) as follows:

STA3000Y Advanced Theory of Statistics (1.0 FCE)

     and two of the following:

STA2101H Methods of Applied Statistics I and STA2201H Methods of Applied Statistics II (1.0 FCE)

STA2111H Probability Theory I and STA2211H Probability Theory II (1.0 FCE)

STA2311H Advanced Computational Methods for Statistics I and STA2312H Advanced Computational Methods for Statistics II (1.0 FCE).

Courses must be chosen in consultation with the advisor and approved by the Associate Chair of Graduate Studies.

Comprehensive Examination Requirements

Within Years 1 and 2, students must complete a two-part comprehensive examination: 1) an in-class written comprehensive exam and 2) a research comprehensive exam.

Students must attempt the in-class written comprehensive by the end of Year 1. If a student fails this portion of the comprehensive exam, one further attempt will be allowed by the end of Year 2. Students who achieve A or A+ grades in all required coursework are exempt from the in-class written exam.

Students must attempt the research comprehensive exam by the beginning of Year 2, which includes a technical report and an oral presentation. If a student fails this portion of the comprehensive exam, one further attempt will be allowed at the end of Year 2.

Students must pass both the in-class written exam and the research exam to continue in the program.

Thesis Requirements

Conducting original research is the most important part of doctoral work. The thesis document must constitute significant and original contribution to the field. Students will have yearly meetings with a committee of no less than three faculty members to assess their progress. The completed thesis must be presented and defended within the Department of Statistical Sciences in addition to being presented and defended at the School of Graduate Studies.

Residency Requirements

Students must also satisfy a two-year residency requirement, whereby students must be on campus full-time and consequently in geographical proximity to be able to participate fully in the University activities associated with the program.

Program Length

Phd program (direct-entry).

Applicants may be accepted via direct entry with a bachelor's degree in statistics from a recognized university with at least an A– average. The department also encourages applicants from biostatistics, computer science, economics, engineering, mathematics, physics, or any discipline where there is a significant quantitative component.

Students must successfully complete a total of 5.0 full-course equivalents (FCEs) as follows:

Year 1: complete 3.0 FCEs:

STA3000Y Advanced Theory of Statistics (1.0 FCE) and two of the following:

Complete an additional 2.0 FCEs at the graduate level. The additional courses must be approved by the Associate Chair of Graduate Studies.

Students must also satisfy a three-year residency requirement, whereby students must be on campus full-time and consequently in geographical proximity to be able to participate fully in the University activities associated with the program.

Field: Actuarial Science and Mathematical Finance

During Year 1, students must complete the following 3.0 full-course equivalents (FCEs) :

(1.5 FCEs) All of:

STA2111H Probability Theory I ,

STA2211H Probability Theory II , and

STA2503H Applied Probability for Mathematical Finance .

(0.5 FCE) One of:

STA2501H Advanced Topics in Actuarial Science or

STA4246H Research Topics in Mathematical Finance .

(1.0 FCE) One of:

STA2101H Methods of Applied Statistics I and STA2201H Methods of Applied Statistics II or

STA2311H Advanced Computational Methods for Statistics I and STA2312H Advanced Computational Methods for Statistics II or

STA3000Y Advanced Theory of Statistics .

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Dalla Lana School of Public Health

• MSc: Biostatistics – Course-​only with Emphasis in Artificial Intelligence (AI) and Data Science Option
• Our Programs

This option was introduced in the Fall of 2019. This is also a course-only option, with the same required courses as the standard course-only option, but where the electives are limited to a subset of courses that emphasize data science and artificial intelligence. This option will be of interest to students wishing to enter the growing field of Data Science, a multidisciplinary science combining elements of statistics and computer science, with roots in mathematics.

Students registered in this option require 5.0 FCE (equivalent to 10 half courses) to graduate. These students are expected to complete the 4.0 FCE required courses outlined in Option 1, as well as 1.0 FCE in approved AI / Data Science electives as outlined below.

Program Requirements

Approved ai / data science electives.

Not all courses are offered every year.  See PHS timetable for current offerings. Please note that the course list is updated periodically.

The Practicum: CHL5207Y Laboratory in Statistical Design and Analysis

This is a full year course with the following format:  i) a weekly 2-hour lecture and ii) a 4-hour per week practicum. Weekly lectures focus on design issues in term 1 and analysis issues in term 2. The practicum occurs at the supervisor’s employment site. To that end, students will be encouraged to integrate themselves as much as possible into their practicum setting.

The main goal of the lecture series is to introduce the student to common statistical design and analysis techniques encountered by the practicing biostatistician/data scientist. The main goal of the practicum is to provide the student with hands-on experience with design and analysis issues encountered by applied statisticians/data scientists in a real workforce setting. It also emphasizes the importance of good communication skills and other soft skills that are required by a biostatistician/data scientist to be effective in today’s work environment.

Note: Students registered in the Emphasis in AI / Data Science must complete their practicum component in the area of artificial intelligence/data science.  The practicum sites will focus on industries that utilize AI and Data Science. The placements will typically run in the summer session, after all the required courses are taken.

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Shahriar Shams

Shahriar Shams is a PhD in Biostatistics candidate at the Dalla Lana School of Public Health at the university of Toronto. His research focuses on applying Bayesian paradigm in reducing uncertainty in the measurement of health utilities. Before joining UTSC, he was teaching at the Statistics department at the St. George campus as a teaching stream limited term faculty. And before starting his PhD program he was working as a Biostatistician at university health network (UHN).

PhD in Biostatistics candidate at the Dalla Lana School of Public Health, University of Toronto.

Teaching Interests

Bayesian statistics, Mathematical Statistics, Regression Analysis, Categorical Data Analysis

Research Interests

• Bayesian Statistics
• Health Utility Measurement

Publications

• Shams S, Pullenayegum EM. Reducing Uncertainty in EQ-5D Value Sets: The Role of Spatial Correlation.  Medical Decision Making . 2019; 39(2):91-99.
• Shams S*, Pullenayegum EM. Design and sample size considerations for valuation studies of multi‐attribute utility instruments.  Statistics in Medicine.  2020; 39(23): 3074-3104.

LMP2004H: Introduction to Biostatistics

Who can attend.

A maximum of 20 students can be enrolled in this course.

Ten of these will be from the MHSc in Laboratory Medicine program , while for the remaining 10 spots priority will be given to students from the research streams at the Department of LMP.

Course description

This course introduces the fundamental concepts of Biostatistics, providing an understanding of the basic theoretical underpinnings and practical applications of statistics.

You will learn essential statistical techniques and analyses relevant to your academic studies or professional work in general medicine and in the fields of pathology and clinical embryology.

Course highlights

Basic theoretical underpinnings : An exploration of core statistical theories including probability, distribution, and inference, creating a strong foundation for further study and application.

Practical applications : Instruction on how to apply statistical concepts to real-world problems in general medicine, pathology, and clinical embryology, offering essential skills needed for basic analysis in these fields.

Basics of AI-assisted coding : Introduction to the principles and techniques of AI-assisted coding, integrating artificial intelligence with statistical methods for enhanced data analysis.

Capstone project preparation : The course provides the necessary statistical foundations for students to successfully undertake and complete the program's capstone projects, integrating theory with practical application.

Overall, this course is designed to equip you with the knowledge and skills to navigate the basic concepts of statistics, enabling you to apply these principles.

Learning outcomes

After completing this course, you will be able to:

• understand the foundational statistical concepts including the mean-variance framework, hypothesis testing, and uncertainty principles, in addition to fundamental equations needed for basic statistical analysis.
• understand study design and statistical applications in medical research.
• produce and appraise a methods section in scientific literature, specifically evaluating and appraising the appropriateness of chosen methods and expressing these methods clearly and correctly.
• produce and appraise a results section in scientific literature, specifically evaluating accuracy of reporting, formatting and structure of reporting, and reporting what is described in the methods section accurately.
• evaluate quality of a statistical model, prediction, or test, and explaining these results to a scientific or non-scientific audience.
• understand statistical coding in R and guidance on AI-assisted code generation, editing, and debugging.

You will be taught in twelve three-hour sessions:

• 1.5 hours: Lectures will cover the foundational theoretical underpinnings of Biostatistics, including core concepts in probability, distribution, inference, practical applications in pathology and clinical embryology, and basics of AI-assisted coding.
• 1.5 hours: Tutorials will offer interactive exercises and problem-solving sessions, focusing on the practical application of the concepts learned in the lectures.
• Participation  will be measured by in class “quick write” assignments which are short concept checks which must be handed in at the end of tutorial.
• Weekly Assignments : will be given at the end of each lecture and will be due at the beginning of the next lecture. These weekly assignments will concentrate on the core concept presented in each lecture, enabling you to reinforce your understanding and practice the application of theoretical principles. The assignments will use provided open-source datasets to develop and report an analysis. 

Late submissions will receive a grade of 0.

Course coordindator

Dr. Mark Tatangelo

[email protected]

Teaching Assistant: 

Julia Gallucci [email protected]

[email protected] for administrative queries.

Timings and location

Wednesdays 9 am – 12 pm

Location: MY 440

Office hours (Tatangelo): Wednesdays 8 - 9 am, MY 440

Office hours (Gallucci): Wednesdays 2 pm, CAMH (250 College St)

Evaluation methods

Participation: 25% (2.5% each “quick write” in class activities)

Weekly Assignments: 25% (2.5% each, best 10 out of 11 for weeks 2-12)

Midterm Exam: 25%

Final Exam: 25% 

Required materials

R version 4.2.3

R Core Team (2022). R: A Language and Environment for Statistical Computing . R Foundation for Statistical Computing, Vienna, Austria.

RStudio Team (2020). RStudio: Integrated Development Environment for R . RStudio, PBC, Boston, MA 

OpenAI (2020). GPT-3: Generative Pre-trained Transformer 3 . arXiv:2005.14165.

Recommended materials 

OpenAI (2021). GPT-4: Generative Pre-trained Transformer 4 . OpenAI. 

Course textbook

Hoffman et al. Basic biostatistics for medical and biomedical practitioners. 2019 

Rosenbaum, P. R. (2010). Design of Observational Studies . Springer Series in Statistics. Springer, New York, NY.

Phillips, N. D. (Year). A Pirate's Guide to R . Publisher.

Course Datasets

Statement on the use of ai.

ChatGPT integration : ChatGPT, an AI model by OpenAI, will be used as a supplementary tool to aid in writing code for R.

Mandatory assignments : There will be specific assignments and modules in which you are required to use ChatGPT. You can access ChatGPT using an email address google account, apple account, or Microsoft account.

Optional use : Outside of mandatory assignments, you may choose to use ChatGPT for other work in LMP2004 as you see fit.

Ethical considerations : Any information or data derived from ChatGPT that is used in student work must be appropriately cited.

Critical analysis : You do not recommend you rely solely on ChatGPT without personal input or analysis. You should assess and validate the information from ChatGPT within the context of your work.

You are expected to follow these guidelines while using ChatGPT for the duration of the course.

Applying to Biostatistics Ph.D. Programs

April 15, 2024

2024   ·   biostatistics   admission   phd  

My application cycle for Ph.D. programs in Biostatistics is finished and I am thrilled to join Brown’s Biostatistics department in the fall!

When I was preparing my applications, I profited from other folks sharing their experiences, especially Kat Hoffman and Simon Couch . Being a first-generation college student, a community college grad, and an international student– I understand how valuable this advice and I want to pay it forward to the next generation of aspiring Biostatisticians.

If you are getting ready to apply, I hope my experiences can help you out. Please don’t hesitate to reach out to me at [email protected] . If you are a non-traditional and/or underrepresented applicant, I would be happy to glance over your work (availability permitting). In your email, briefly describe why you are seeking what specific advice.

A Preliminary Disclaimer:

Please note that this post represents my opinions alone. My situation and circumstances may be quite different from you. In that sense, please take all opinions with a grain of salt. When stating/ recalling facts, I will do so to the best of my availability. Please reach out to me if you find any factual inaccuracies.

My Background

I am originally from Munich, Germany and I graduated from a large public state school with degrees in Economics, Business Analytics, and a minor in Mathematics. Before that, I completed an Associate’s degree in Business Administration at a community college.

After graduation, I briefly worked at an economic research consultancy focusing on energy economics. Coming out of undergrad, I was dead set on pursuing a Ph.D. in Economics, so when I was offered a pre-doc position at the Energy & Environment Lab at the University of Chicago , I jumped on it. I worked at the intersection of causal inference and machine learning there, although my work was not very technical (i.e. I was applying existing methods to new data sets rather than developing new statistical methods). Throughout this time, I became more and more fascinated with Statistics.

After 8 months, I decided to leave the E&E Lab and start my studies as a non-degree-seeking graduate student at the University of Chicago. The rationale was that I needed some more rigorous coursework (e.g., analysis) under my belt to be a competitive candidate for Ph.D. programs in (Bio-)Statistics.

If you want a more comprehensive overview, check out my CV here (Note that this is my current CV, not the one I submitted. Keep scrolling if you are interested in the CV I submitted).

My Interests and Goals

I am interested at the intersection of causal inference and statistical network analysis. In particular, I care about causal inference in high-dimensional networks, non-parametric and assumption-lean methodology, and dynamic treatment regimes. Ultimately, I would like to apply my work at the interface of public health and climate, helping policymakers make more informed decisions in response to environmental disasters and global warming.

With respect to my post-Ph.D. goals, I oscillate around the following numbers:

• A career in academia: 55%
• A research career in a public/ think tank role: 43%
• A career in private industry: 2%

When it came down to sending out my applications, I ended up applying exclusively to Biostatistics Ph.D. programs (with the exception of one program). Here is why:

• Biostatistics allows me to be a statistician while maintaining my applied interest in public health and environment
• Biostatistics programs tend to be much more causal inference focused than traditional Statistics Ph.D.’s
• Biostatistics programs are highly interdisciplinary, something I really value

My Weaknesses

• Undergraduate background : I majored in neither Statistics nor Math in undergrad. Although my coursework was certainly not completely unrelated to (Bio-)Statistics, I know that competing with math and stat majors from Ivy+ universities was going to be challenging. On that note, my undergraduate institution is not particularly renowned in Statistics.
• Recommendations : This is a mixed bag but I actually didn’t end up with a single UChicago recommender. Only one of my recommenders (who was my professor for my graduate ML course senior year) was a Statistician (+1 Economist, +1 Operations Researcher). (Not so) fun fact: I was going to get a recommendation from a UChicago professor but I did so poorly on the midterm that we decided a letter from him wouldn’t be wise lol (So keep your head up; you’re allowed to have accidents). Another thing that was less than ideal for me is that I did not get a letter from the UChicago lab I worked at. Lab policy dictates that you have to stay a certain amount of time to be eligible for a letter and I did not do that–that probably wasn’t great.
• Graduate coursework : I was enrolled in a full-time non-degree-seeking program taking graduate coursework, but that is not the same as an MS in Statistics.

My Strengths

• My Publication : I have a single-author peer-reviewed publication from undergrad. Though the paper doesn’t propose any new statistical methodology, it does focus on the application of some interesting methods in novel ways and applies them to data. I think that paper really helped my case. Moreover, the professor who supervised this thesis (an economist) wrote me a kick-ass letter of recommendation.
• Research Experience : Ironically, my application profile was the inverse of most other (“traditional”) applicants. Usually, applicants to (Bio-)Statistics Ph.D.’s are math/stat undergrad majors from very respectable institutions with little to no research experience. My profile was the opposite of that. I had almost two years worth of full-time research experience but a lack of formal preparation. I think my research background in causal inference and ML (in addition to my publication) were my strongest assets.
• Grades : I had excellent grades throughout my undergrad. During my first quarter at UChicago, I got two “A-“ and one “Pass” (Injury related). The latter is respectable though certainly not outstanding.
• Leadership Experience : I do think that my leadership roles–e.g., founding a successful data science / social justice org in undergrad–was a big bonus on my application.

The Application Process

I went into this application cycle with the mentality of “giving it a shot”. I was fully prepared to get rejected by all programs because I thought my strengths did not quite outweigh my weaknesses. With that in mind, I applied to both masters degrees (Only in Canada for funding and personal reasons) and my top Ph.D. programs. Here is the list of the places I applied to:

I applied to the UChicago Data Science Ph.D. mostly because I was currently working at the UChicago Data Science Institute and I knew a good amount of the faculty. I applied to Masters only in Canada because (1) I was eligible for German government funding in Canada but not the U.S., (2) For personal reasons, and (3) Overall cost and quality of life. Additionally, I was a finalist for a full-ride leadership-based scholarship at McGill.

You may also notice that I didn’t apply to many other top-ranked departments. This is because I either found little research fit or (this was mostly the case) I didn’t want to live wherever the school was located. I encourage you to not sweep this factor under the rug.

I also had an internal ranking. The following factors were most important to me:

• Substantial research in causal inference and networks: #1 JHU, #2 Brown, #3 UW, #4 Yale
• Location and access to nature: #1 UW, #2 Berkeley
• Research fit with individual faculty: #1 JHU, Berkeley, Brown, Yale, #2 Harvard

One more thing: Since I was ready to get rejected from all programs, I was working towards being a more competitive applicant during the next cycle while I was applying. In that sense, I had started a graduate research assistantship at UChicago in causal inference methodology and started TA’ing. This forward-looking approach really helped me mentally since I kept reassuring myself that I can just try again next year.

Preparation

First things first, I decided to take the GRE. I performed slighly above average, but nothing outstanding. Overall, I found the GRE to be a colossal waste of time, money, and energy. If I could redo my application cycle, I would have opted to not take it and scratch the programs that require it (Only two of them) off my list. After all, with all the evidence that is out there showing how the GRE puts marginalized students at a disadvantage, merely requiring the GRE is a huge red flag for me.

I started preparing my application materials very early, around June, because I knew I had a ton of time over the summer (as opposed to little to no time in the fall). One thing I did that made life significantly easier for myself and my recommenders is to start a GitHub repository called grad-apps that contained all my application materials. At the time I was writing my applications, this repo was public, so all my recommenderes and mentors could have easy access to it. It is now private (and will remain private) for privacy reasons.

That being said, I am happy to share how I set it up. Here is content of the README.md file that sketches out the basic setup:

Hi! If you are reading this, I want to thank you for helping me in my graduate school application process. Thank you so much for your support.

• You can navigate this repository via the branches
• Please access my most up-to-date CV and this README via this main branch
• Each program I am applying to has a corresponding branch with the following format: [institution]-[type]-[program], e.g., “uw-phd-biostatistics” for the University of Washington’s Biostatistics Ph.D.
• Since this is a public repo, please note that you do not have editing access. If you have comments/ suggestions, please do not directly edit these materials. Instead, please use the comment function and/or let me know separately.

Please note that I update this repo every time I make local changes. If you have any questions at all, please reach out to me via email or via cell.

[Followed by a table of programs with every deadline]

My Application Materials

I have decided to post my application materials for Brown’s Biostatistics Ph.D., the program I will be attending in the fall. It should go without saying that you should under no circumstances copy and paste from my materials. That being said, I know that sometimes it is difficult to find good examples of SOP’s, personal statements, and CV’s. If there are any additional programs that you would like to see my application materials for, please reach out to me via email .

Now, I will provide some commentary and context on these application materials. I was extremely lucky to have a handful of faculty and UChicago’s GRAD advising staff give me feedback. With that being said, please don’t treat these materials as the gold standard. They are by no means perfect.

• Education, Coursework, Awards : I initially had grades on there as well (Nothing worse than an A-) but a professor recommended against that because the “A-“ could catch somebody’s eye early. I know how nitpicky this sounds but I am just echo’ing what he said here.
• Research Experience and Community Service : Some folks said that my CV boasts a lot of details but that was on purpose. My research experience was my strongest asset and I wanted to highlight everything I did.
• Presentations, Professional Service, Skills : Having sections for this is not necessary for Ph.D. applicants because you may not have gathered any substantial experiences yet. I did have some space to spare though, so I decided to include some of the presentations and talks I had given. One more thing: Don’t forget to include your programming skills! I know some programs, e.g., UW Biostatistics, explicitly require that on your CV.

SOP and Personal Statement

• General : My SOP’s were identical across all programs I applied to with the exception of two paragraphs (Of course I adjusted the name of school in the other paragraphs lol). Generally though, I wanted to lead with an eye-catcher (the bolded sentence in the first paragraph). Then, I wanted to get addressing my main weakness (the lack of rigorous coursework) out of the way (that’s paragraph #2). Up to paragraph #6, I describe my research experience and how it led to my current interest.
• Paragraphs #7 and #8 : I use paragraph #7 to talk about how my interests tie into the program/department/faculty members. A lot of times, people will put this paragraph as the second paragraph which may work very well with your application. The last paragraph is similar to seventh paragraph but focuses more on research community, centers, and personal fit.
• Personal Statement : This was identical across all schools I applied to.

Overall, I think that being very neat and submitting “pretty” application materials was a tiny Brownie point for me. I would recommend submitting neat materials to anyone and if you can use something like LaTeX you may make an extra good impression.

After submitting all my applications and trying to financially recover, the waiting game began. Pretty soon after winter break was over, I heard back from Brown. They wanted to have my autumn quarter transcript. About two days after I sent that over, I was officially invited to interview day.

The interview day (virtual) was an all-day thing, filled with various info sessions and interviews. I know that about 30 people out of ~380 applicants (which is an insane number) were invited to interview day. Here is a rough outline of my schedule:

• Info session for all the shortlisted candidates across all the Ph.D. programs in the School of Public Health (incl. Epidemioligy, Biostatistics, etc.)
• Departmental info session specific to Biostatistics
• 3-4 interviews with faculty. One interview was with a member of the admissions committee (30min). Additionally, you were allowed to choose up to 3 faculty members for a 15min interview. Since my 30min interview happened to be with a professor I was interested in working with, I ended up choosing only two additional faculty. Thus, I had a total of 3 separate faculty interviews.

I talked to some folks around preparation and decided to go a bit “lighter” than usual. When I am nervous in interviews (which you bet I was), I tend to jump into rabbit holes and try to impress my knowledge. That is generally not a good strategy since you’re being interviewed to assess how you are as a person and the faculty member you’re interviewing with could choose to grillyou on something you said. Keep in mind that you made it to the interview stage because the admissions committee is already impressed with your qualifications on paper . With that in mind, here is how I prepared:

• Researched each interviewer’s active grants (Important in Biostatistics especially) and what the project was about. Wrote out 2-3 questions about that work.
• Researched each interviewer’s fields of interest and recent work and crystallized out one or two overlapping interests. I didn’t fully read through any person’s paper because of the reason I stated above.
• Looked into each interviewer’s story to look for common ground (If there is something striking that could be a phenomenal ice breaker)

After having prepared, here is how I experienced the interview:

• Very relaxed and cordial atmosphere. I was not grilled on any technical questions though I was asked a specific (but very fair) question about my published paper.
• It felt like a true conversation where faculty seemed to be most interested in who I am as a person and less about my qualifications.
• It seemed that my interviewers really appreciated that I knew about their active grants. Of course, they don’t expect you to know everything about it (that’s their job, after all) but just showing that you did your “homework” makes a great impression.

After I got done with the interviews, I had a feeling that I did really well.

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

The Waiting Game

Ironically, despite an overwhelming feeling that did super well, I did’t hear back for a really long time. In the previous stages, Brown had been very quick, so I expected a decision within 2 weeks of the interview. One month, then two months passed–and nothing. It was really tough on my mental health because I had only heard back negative news from the other programs so far and I was losing hope. Please make sure to take care of yourself while you wait. For me, working out and going to therapy were two great outlets.

Then, around mid March, I got the email and call that I was admitted. I had gotten off the waitlist.

My advice to you while you wait: Please don’t tie your personal value into these applications. I knew coming in that I was an excellent candidate but also how stiff the competition was. Ph.D. admissions truly are a blackbox and as long as you do everything in your power to maximize your chances, you should be very proud of yourself.

Decision Time

My final results.

As of April 15th (Decision day), these are my results from the admissions process:

As you can see, it was a tough cycle. I was very surprised by some rejections and less so by others. One important thing I want to mention: I believe I got rejected from most programs because I was not a “traditional” math/stat undergrad from a great school. There is only so much I can do to remedy that ex-post. I decided to still share this post because I had been told from faculty and mentors that the materials themselves were strong. Remind yourself that it only takes one singular program to admit you.

My Decision

Brown ended up flying me out and organizing a visit day. I felt like it was a particularly strong match because of the cordial and down-to-earth department culture. I was at the time still debating between McGill and Brown–since I could have (with an almost certain guarantee) transferred into the Ph.D. program after Year #1.

My decision ended up coming down to:

• Research fit: I can work at the intersection of causal inference and networks.
• Stipend and benefits: Brown pays one of the highest stipends I have heard of.
• Departmental/ Culture fit: The department seems amazing and my gut is telling me I will be very happy there.

Funding is an important thing to consider, as well. Brown was amongst the top-paying (if not the top-paying) schools out of my list. In my admissions offer, alongside health insurance and full tuition, I am guaranteed a stipend of $49,012 and a one-time first-year supplement stipend of $1,750. When evaluating funding decisions, it does sometimes help looking at whether the school’s graduate students are unionized (Brown’s are).

Final Thoughts

Overall, I am very happy with how things went. I came into this process having two clear favorites–UW and Berkeley Biostatistics–and am coming out very satisfied despite getting rejected by both. This process is very intimidating and I am hoping that my thoughts add a little bit of clarity. If you are a non-traditional applicant, I invite you to reach out to me to have a chat (Caveat: Come fall, I will likely have very limited availability, but I would be happy to chat over the summer). Lastly, please reach out to me if you have any burning questions so I can answer them here.

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Career development – upcoming events.

Career Development Series It is that time of year when students and faculty alike are preparing for the end of their semester courses, and many students are defending PhD dissertations or … Continue reading “Career Development – Upcoming Events”

Thanks to all who have been participating! We are learning a lot from each other and having some great discussions amidst this very challenging time. If you have input regarding … Continue reading “Career Development – Upcoming Events”

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Women in Global Health Leadership Fellowship Inaugural Programme

The Women in Global Health Leadership Fellowship (WGHLF), a part of the Healthy Futures South Africa project in the Faculty of Health Sciences, kicked off its inaugural in-person training in Cape Town, South Africa, from March 10th to 15th, 2024.

The article provides an overview of experience of the programme’s first in-person block week. The inaugural programme has six participants from the Western Cape, five from the Department of Health and Wellness working at different levels of the health system and one of our own lecturers from the School of Public Health.

The WGHLF is run in collaboration between the Centre for Global Health at the University of Toronto’s Dalla Lana School of Public Health, Moi University’s School of Public Health in Kenya, and the University of Cape Town School of Public Health in South Africa, in partnership with the Mastercard Foundation.

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.