Entering students are assumed to have the necessary mathematical background including at least one semester of multivariate calculus, one semester of linear algebra and one semester of mathematical probability using calculus prior to attending Brown. 

The Master of Science degree in Biostatistics requires 10 courses (7 required and 3 elective) in biostatistics, in addition to the mandatory PHP 1001 Public Health (a multi-module, online introduction to public health course required by all School of Public Health students).  The Program’s biostatistics sequence is configured as 3-3-2-2 and must be followed for this degree program. The specifics are as follows:

Required Courses - Sc.M. (7 biostatistics plus PHP 1001)

PHP 2515  Fundamentals of Probability & Statistical Inference OR PHP 2520 Inference I

PHP 2514 Applied Generalized Linear Models

PHP 2516 Applied Longitudinal Models (½ Course)

PHP 2517 Applied Multilevel Models (½ Course)

PHP 2550 Practical Data Analysis

PHP 2560 Statistical Programming with R

PHP 2610 Causal Inference & Missing Data

PHP 2650 Statistical Learning/Big Data

PHP 1001 Scope of Public Health (online course)

Elective Courses (choose 3)

Statistical Electives:

PHP 2030 Clinical Trials Methodology

PHP 2530 Bayesian Statistical Methods

PHP 2580 Statistical Inference II

PHP 2601 Linear Models

PHP 2602 Analysis of Lifetime Data

PHP 2605 Generalized Linear Models

PHP 2620 Statistical Methods for Bioinformatics

PHP 2980: Graduate Independent Study & Thesis Research

PHP 2590: Design of Experiments

PHP 2670: Simulation Models for Public Health Decision Making

Epidemiology Electives:

PHP 2120 Introduction to Methods in Epidemiologic Research

PHP 2150 Foundation in Epidemiologic Research Methods

PHP 2200 Intermediate Epidemiologic Methods

Programming and Data Science Electives:

PHP  2561 Methods in Informatics and Data Science for Health

CSCI 1420 Machine Learning

CSCI 1470 Deep Learning

CSCI 1570 Design and Analysis of Algorithms

CSCI 1810 Computational and Molecular Biology

CSCI 1820 Algorithmic Foundations in Computational Biology

For a full list of classes that the Department of Biostatistics offers please visit our Courses page.

Thesis/Project

The requirements for the Sc.M. program include the coursework listed above PLUS a Master’s thesis or project. Students may choose to do their thesis for credit using PHP 2980 as one of their electives or may choose to do the thesis without credit while using their 3 electives for coursework. It is also possible to use PHP2980 as an independent study unrelated to the thesis.    

All students in the program will be required to write a project/thesis on a topic in one of the following areas: development of a new analytic method, detailed study of an existing method or comparison of performance of different methods (e.g. simulation studies); development of new software packages for statistical programming including a published repository, documentation and vignettes; or review or synthesis of a new or emerging statistical methodology or application.

All projects and thesis will require a significant amount of work and a written document. A thesis must follow the Brown Graduate School Guidelines and be published in this manner. Projects allow for students to engage in work that is just as rigorous but is not sufficient for submission for publication. For example, many students might be involved in preliminary analysis of clinical trials or other data which they are not allowed to publish at the time of the graduate school deadline. Or, the work may be a detailed analysis of data that forms part of a larger, publishable work.

Students should choose a thesis/project advisor from the Biostatistics faculty by the end of their first year. This advisor may also serve as the student’s academic advisor if both agree. In addition to a faculty advisor, the thesis/project requires one reader who will also sign off on the finished product. The reader does not need to be a Biostatistics faculty member, but could be an outside Brown faculty member, faculty from another institution, or a scientist with whom the student is working on the project/thesis. For example, the student might develop a project out of work being done through an internship at a hospital and might have the clinical supervisor serve as the reader.

Glimpse Into the First Semester

In the first semester, incoming students take three courses that set up a foundation inference, modeling, and programming. Check out what our current students are saying about these courses below!

PHP 2515: Fundamentals of Probability & Statistical Inference

This course provides an introduction to probability theory, mathematical statistics and their application to biostatistics with an emphasis on the mathematical and probabilistic concepts that form the basis for statistical inference.

1. "Fantastic foundational statistical inference course, be prepared to learn a lot!"
2. "The structure of the course is well organized and the topics appropriately build upon each other. Problem sets had a perfect balance of challenging questions to reinforce concepts and force you to connect concepts across topics."
3. "This course is an excellent choice for anyone interested in advancing their understanding of probability, statistics, and statistical inference. The concepts tackled are all very necessary in understanding statistics and its applications in various fields."'

PHP 2514: Applied Generalized Linear Models

This course provides a survey of generalized linear models (GLMs) for outcomes including continuous, binary, count, survival and correlated data. This course will work through the basic theories of GLMs. Emphasis will be on understanding the implications of this theory and the applications to solving real data problems.

1. "This course is one of the most important courses I have taken in my academic career. It gave us training in important generalized linear models for health research, procedures for the analytical process (exploratory data analysis, model selection procedures, assumption checking, interpretation), and most importantly, taught us to be independent biostatisticians. I now feel ready and excited to take any dataset presented to me, generate my own research question, and answer it!"
2. "This is an excellent course for anyone interested in gaining hands-on experience in generalized linear models and their applications to solving problems in the real world."

PHP 2560: Statistical Programming in R

The flipped course introduces fundamental concepts of programming in R: data types, data cleaning and manipulation, visualization, loops, functions, running simulations, and creating web applications. This course will ask you to think in a computational manner by giving a peek under the hood of R, focusing on key building blocks,and developing good coding practices. 

1. "I can't say enough good things about the way this course was taught.... I really enjoyed that we had pre-class portions and instead spent class time applying the skills we learned. This is probably the most effective way to teach a programming class. I felt both challenged and supported in this class. Finally, the midterm and final projects were great opportunities to synthesize what we learned throughout the course. Overall, I'm really proud of the skills that I learned!"
2. "I enjoyed the structure of the class as well as how the labs were both interesting, challenging, and allowed us to learn the programming language."

Individual Development Plan (IDP)

In response to the National Institutes of Health (NIH) notice NOT-OD-13-093 and the Brown University School of Public Health mandate regarding the use of Individual Development Plans (IDP), all students in the Department of Biostatistics, regardless of funding sources, are required to complete and submit, in consultation with their advisor, and IDP. Specifically:

• Incoming, matriculating students must complete an IDP, in consultation with their advisor, by the beginning of their second semester.  
• All students must submit an updated IDP, in consultation with their advisor, on an annual basis.  

The IDP is a valuable tool that gives students the opportunity to consider and address their short-term and long-term career goals.  In order to achieve compliance with the IDP policy, please fill out the Individual Development Plan for Biostatistics, discuss with your advisor, and submit your completed form. 

Brown Biostatistics graduate outcomes from 2019 to 2023. We had 59 graduates respond to our exit survey. 100% of the respondents are either pursuing a higher education degree or are employed in various sectors!

Thomas Arnold

Master's entered 2023

Title: Loss of control eating in relation to blood pressure among adolescent girls with elevated anxiety at-risk for excess weight gain

Abstract: Loss of control (LOC)-eating, excess weight, and anxiety are robustly linked, and are independently associated with markers of... Continue Reading

Han Ji

Master's entered 2022

Title: causalBETA: An R Package for Bayesian Semiparametric Causal Inference with Event-Time Outcomes

Abstract: Observational studies are often conducted to estimate causal effects of treatments or exposures on event-time outcomes... Continue Reading