Epidemiology and Biostatistics: Biostatistics, PhD

The PhD program in biostatistics is designed to prepare students to be independent researchers in the development of statistical methodologies and in the appropriate and innovative application of these methodologies to biomedical research problems.  In the first five semesters of the program, students complete a series of courses in both theory and applied methodology, engage in individually mentored research experiences, explore statistical collaboration, and complete the qualifications examination. Within this period, students also identify a dissertation research problem and an advisor and present a research proposal as part of the candidacy examination.  Students typically defend their dissertations and graduate within five years of matriculation.

View the University’s Academic Requirements for PhD Degrees.

Required Courses 

Coursework
Theory Courses
BSTA 620Probability I
BSTA 621Statisical Inference I
BSTA 622Statistical Inference II
Methods Courses
BSTA 630Statistical Methods and Data Analysis I
BSTA 632Statistical Methods for Categorical and Survival Data
BSTA 651Introduction to Linear Models and Generalized Linear Models
BSTA 656Longitudinal Data Analysis
BSTA 660Design of Observational Studies
BSTA 661Design of Interventional Studies
BSTA 670Statistical Computing
BSTA 754Advanced Survival Analysis
BSTA 511Biostatistics in Practice
Additional Coursework
Electives
BSTA 751Statistical Methods for Neuroimaging
BSTA 771Applied Bayesian Analysis
BSTA 774Statistical Methods for Evaluating Diagnostic Tests
BSTA 779Semiparametric Inference
BSTA 782Stat Meth/Incomplet Data
BSTA 783Multivar/Funct Data Anal
BSTA 785Stat Meth/Genomic Data
BSTA 786Adv Topics/Clin Trials
BSTA 787Methods for Statistical Genetics and Genomics in Complex Human Disease
BSTA 788Functional Data Analysis
BSTA 789Big Data
BSTA 790Causal Inference in Biomedical Research
Research Requirements
BSTA 699Lab Rotation
BSTA 899Pre-Dissertation Research
BSTA 995Dissertation

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2020 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.


Sample Plan of Study

Year 1
Fall
Probability I
Statistical Methods and Data Analysis I
Design of Observational Studies
Design of Interventional Studies
Lab Rotation
Spring
Statisical Inference I
Statistical Methods for Categorical and Survival Data
Introduction to Linear Models and Generalized Linear Models
Lab Rotation
Summer
Lab Rotation
Year 2
Fall
Statistical Inference II
Advanced Survival Analysis
Longitudinal Data Analysis
Biostatistics in Practice
Lab Rotation 1
Pre-Dissertation Research
Spring
Statistical Computing
Advanced Elective/Minor
Lab Rotation 1
Pre-Dissertation Research
Elective
Year 3
Fall
Pre-Dissertation Research
Elective
Spring
Dissertation
Year 4 and Beyond
Dissertation
Elective