Graduate Group in Epidemiology & Biostatistics

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PhD Program in Biostatistics


The PhD program aims to train independent researchers in biostatistics applications and methodology. The program includes the Master's program courses, comprehensive analysis of a dataset and reporting of results equivalent to the MS thesis, and at least one additional semester of advanced courses in statistical theory and methods. Students in the PhD program are also required to take courses toward a minor in biomedical science, pass both written and oral examinations, and successfully complete a doctoral dissertation. An overview of the program appears here; for complete details see the GGEB Handbook

Academic Advisor

Each incoming PhD student is assigned an academic advisor who serves as the student's primary mentor during the first two years of the program, advising in course selection and related academic matters. Once a thesis advisor is selected, s/he will normally assume the role of academic advisor for the remainder of the student's program. The Graduate Program attempts to assign advisors to students with similar backgrounds and interests. A student may petition to change academic advisors at any time by request to the Director of Graduate Studies.

Course Requirements

The PhD in Biostatistics typically requires five to six semesters of coursework and additional semesters devoted to dissertation research.  This is usually accomplished in four to five years of full-time study.  The standard course sequence for PhD students consists of 3 units in theory, 7 units in statistical methods, 2 units toward a minor and 3 units of electives in advanced theory and methods. In addition, a minimum of three semesters of lab rotations (BSTA 699) are required.  In general, students are expected to have completed all required courses by the end of their 3th year (or equivalent for those who enter with a Masters degree).  In rare cases substitutions may be made.  Such alternatives must be pre-approved by the chair of the Curriculum Committee, the Program Chair, GGEB Chair and the director of the course being waived, who is in the best position to evaluate whether the necessary skills are met by the substitution.

Below are the required core courses; the courses in bold type are PhD “core” courses that are covered on the written qualifying examination.


BSTA 620: Probability (1 unit)

BSTA 621: Statistical Inference I (1 unit)

BSTA 622: Statistical Inference II (1 unit)


BSTA 630: Statistical Methods and Data Analysis I (1 unit)

BSTA 632: Statistical Methods for Categorical and Survival Data (Methods II) (1 unit)

BSTA 651: Introduction to Linear Models & Generalized Linear Models (1 unit)

BSTA 656: Longitudinal Data Analysis (0.5 unit)

BSTA 660: Design of Observational Studies (0.5 unit)

BSTA 661 Design of Interventional Studies I (0.5 unit)

BSTA 670: Statistical Computing (1 unit)

BSTA 754: Advanced Survival Analysis (0.5 unit)

BSTA 511: Biostatistics in Practice I (1 unit)

Electives and Independent Study

Students are required to take 3 additional advanced electives; a partial listing of such courses is given below.  In addition to this list, other courses offered by departments outside of Biostatistics and Epidemiology may be appropriate advanced electives and may be used as an advanced elective for the PhD program upon receiving approval from the student’s academic advisor and the Program Chair.  Independent study or reading courses (BSTA 999) are reserved for doctoral students who have passed the written qualifications examination and are either choosing a dissertation topic or undertaking the early stages of dissertation research.  At most one of the four required advanced electives may be a reading course, and only on a topic not offered as a formal course with a year.

STAT 530: Probability (1 unit)
STAT 531: Stochastic Processes (1 unit)
STAT 921: Observational Studies (1 unit)
STAT 925: Multivariate Analysis: Theory (1 unit)
OPIM 930: Stochastic Models II (1 unit)
BSTA 751: Statistical Methods for Neuroimaging (1 unit)
BSTA 771: Applied Bayesian Analysis (1 unit)
BSTA 774: Statistical Methods for Evaluating Diagnostic Tests (0.5/1 unit)
BSTA 775/STAT 920: Sample Survey Methods (1 unit)
BSTA 782: Statistical Methods for Incomplete Data (1 unit)
BSTA 783: Multivariate and Functional Data Analysis (1 unit)
BSTA 785: Statistical Methods for Genomic Data Analysis (1 unit)
BSTA 786: Advanced Topics in Clinical Trials (1 unit)
BSTA 787: Methods for Statistical Genetics in Complex Human Disease (1 unit)
BSTA 788: Functional Data Analysis (1 unit)
BSTA 789: Big Data (1 unit)
BSTA 790: Causal Inference in Biomedical Research (1 unit)
BSTA 820/STAT 552: Statistical Inference III (1 unit)
BSTA 852/STAT 910: Forecasting and Time Series (1 unit)
BSTA 854/STAT 927: Bayesian Statistical Theory and Methods (1 unit)

Applied Research Requirement, Equivalent of MS Thesis

All PhD students must participate in Biostatistics in Practice and complete a Biostatistics in Practice project, a requirement that students typically satisfy during the first or second year.  See Section 7.3.9 for further details.

Teaching Practicum

All students in the PhD program must provide teaching support for a course or courses offered by the Department of Biostatistics and Epidemiology or related programs.  This is discussed in detail in Section 7.5.

Weekly Seminar

A fundamental component of the PhD program is attendance at the weekly Biostatistics Seminar. All PhD students are required to attend this seminar series. Students are also encouraged to suggest experts from the field as potential seminar speakers to the Seminar Committee.


Students must complete a two-unit minor sequence in one or more areas of science relevant to biomedical research. Some possible subject areas for minor courses include epidemiology, genetics, biology, psychology, economics, computer science, and bioengineering. Minor courses are typically taken outside of the GGEB, with the exception of advanced epidemiology courses (beyond BSTA 509) which may also be counted toward the minor. The two-unit minor sequence must be approved by the curriculum committee chair and the program chair.

Qualifications Evaluation Examination

A written qualifications evaluation examination covering material in the required courses for the PhD degree is offered each summer, in June. All full-time PhD students are expected to sit for the examination after their first year of study, with the option to retake the exam the following year if needed.  No student is allowed to take either part of the exam more than twice. Students must pass this exam to continue in the graduate program.

Candidacy Examination

To become a formal candidate for the PhD, a student must pass a candidacy examination, which generally focuses on the proposed dissertation research (see below) but may also cover related topics in biostatistics and the minor field. For additional information, see the University of Pennsylvania Graduate Catalog.

PhD Thesis

The PhD thesis is an original contribution to statistical methodology for biomedical applications. This can be accomplished either through a novel application of statistical methods to a given subject matter, the development of new statistical methods or theory, or a combination of new theory, methods and applications. Dissertation research culminates in a final dissertation examination, or thesis defense, which consists of an oral presentation by the candidate and an examination by the faculty. For details, see the Graduate Catalog.

Lab Rotations

Goals and Objectives
The overall goal of the rotations is to expose students to biomedical research, and in particular research related to statistical methodology early in their training.  In addition, students will rotate through a number of different labs, in order to get a broad perspective on research and faculty.   This will also assist the students in identifying their research interests and dissertation topic earlier in their educational process.  In addition, both the students and faculty can assess whether they are a good match for possible dissertation advisor/advisee relationships. By the end of 21 months of training (summer of year 2) students who were initially funded by BGS will identify their dissertation advisor, have a foundation for the first topic in their dissertation work, and move off of BGS funding and onto funding that is related to their dissertation work. Students will normally identify their PhD mentor through working with them on a lab rotation.  Students who are funded by a training grant during their first 21 months in the program will remain on the training grant throughout their program.  Students who are currently funded or who have interests in receiving funding during their dissertation research from a training grant should discuss how to structure their lab rotations with the training grant director. Lab rotations that offer research experience in areas relevant to our training grants will be available each year.

Transfer of Credit

Only courses considered at the graduate level may be transferred from previous training. A maximum of eight units may be transferred from previous training towards the PhD degree.  Courses proposed for transfer credit must be relevant to training in biostatistics and may include courses in theory, methods, or towards a minor (see Section 7.3.6 regarding minors).  Transfer of credit must be approved by the Program Chair and the GGEB Chair.

Academic Program Proposals and Approvals

At the end of each academic year, each student, in collaboration with his/her advisor, are reqjuired to prepare an individual development plan (IDP). The IDP will include a proposal for the academic program including courses to be taken, courses to be transferred, and timelines for examinations and thesis preparation.


Typical Course Sequence for Full-Time Students in the PhD Program Entering Fall of 2018


Semester 1







Year 1



FT Courses:

BSTA 620: Probability (1)
BSTA 630: Statistical Methods and Data Analysis I (1)
BSTA 660: Design of Observational Studies (.5)
BSTA 661: Design of Interventional Studies (0.5)
BSTA 699: Lab Rotation



FT Courses:

BSTA 621: Statistical Inference I (1)
BSTA 632: Statistical Methods for Categorical and Survival Data (Methods II) (1)
BSTA 651: Linear Models and Generalized Linear Models (1)
BSTA 699: Lab Rotation


Written Qualifications Examination

BSTA 699: Lab Rotation





Year 2



FT Courses:

BSTA 622: Statistical Inference II (1)
BSTA 754: Advanced Survival Analysis (0.5)
BSTA 656: Longitudinal Data Analyisis (0.5)
BSTA 511: Biostatisitcs in Practice Project (1)
BSTA 699: Lab Rotation or BSTA 899: Pre Dissertation Lab Research (if a dissertation advisor has been chosen)



FT Courses:

BSTA 670: Statistical Computing (1)
Advanced Electives/Minor (2)
BSTA 699: Lab Rotation or BSTA 899: Pre Dissertation Lab Research (if a dissertation advisor has been chosen)

Biostatistics in Practice Project / MS Thesis Presentation


BSTA 899: Pre Dissertation Lab Research

Oral Candidacy Examination
PhD Thesis*





Year 3



FT Courses:

Advanced Electives/Minor (3)
BSTA 899: Pre Dissertation Lab Research

Oral Candidacy Examination (if not completed in summer)
F31 Grant Proposal
PhD Dissertation



FT Courses:

BSTA 995: Dissertation Research

F31 Grant Proposal (if not completed in fall)
PhD Dissertation



PhD Dissertation


Years 4-5


FT Courses:

BSTA 995: Dissertation Research

PhD Dissertation


*Teaching requirement; typically completed in years 2–5.