Overview

The goal of the MS program is to train students in the basic theory and applications of statistical methods, as applied to problems in the biomedical sciences. The program typically consists of two years of full-time study, including the the preparation of a Master's thesis. Requirements include courses in probability, mathematical statistics, and statistical methods including discrete data analysis, linear models, multivariate methods, survival analysis, statistical computing, and applied data analysis. All students take a course in epidemiology. An overview of the program appears here; for complete details see the GGEB Handbook.

Academic Advisor

Each incoming MS student is assigned an academic advisor who serves as the student's primary mentor during the program, advising in course selection and related academic matters. An attempt is made to assign advisors to students with similar backgrounds and interests, although a student may petition the Director of Graduate Studies at any time to request a change of academic advisor.

Course Requirements

Candidates for the MS degree must complete 12 units of course credit and prepare a Master’s thesis.  Required courses cover probability, mathematical statistics, and statistical methods including linear models, longitudinal data analysis, survival analysis, statistical computing, and applied data analysis.  MS students will have the option to take the written qualifying exam but it is not required to obtain a masters in biostatistics.

The MS in Biostatistics typically requires four semesters of formal course work.  Students must complete nine units of required courses, three units of electives, and the Biostatistics in Practice and project requirements (see Section 7.3.9). The required courses are described below.  The courses in bold type are the “core” courses for the MS degree that are covered on the written qualifications examination.

• Theory:

  • BSTA 6200: Probability (1 unit)
  • BSTA 6210: Statistical Inference I (1 unit)

• Methods:

  • BSTA 6300: Methods I (1 unit)
  • BSTA 6320: Statistical Methods for Categorical and Survival Data (Methods II) (1 unit)
  • BSTA 6510: Introduction to Linear Models & Generalized Linear Models (1 unit)
  • BSTA 6560: Longitudinal Data Analysis (0.5 unit) 
  • BSTA 6600: Design of Observational Studies (0.5 unit)
  • BSTA 6610: Design of Interventional Studies I (0.5 unit) 
  • BSTA 6700: Statistical Computing (1 unit)
  • BSTA 7540: Advanced Survival Analysis (0.5 unit) 
  • BSTA 5110: Biostatistics in Practice (1.0)

General Examination

The written qualifying examination is optional for MS students. This exam is offered each summer, in June, in conjunction with the Qualifications Evaluation Examination for the PhD.

Elective Courses

Students in the MS program choose three additional units from a list of advanced courses in biostatistics and related topics.  At least two of these courses must be quantitative; the third may be in a related scientific field subject to approval by the Curriculum Committee Chair and Program Director.  A partial listing appears under the section on electives for the PhD program (Section 7.3.3).  In addition to these electives, BSTA 6220 Inference II and BSTA 7540 Advanced Survival Analysis, which are required courses for the PhD program, may be used as advanced electives for the MS program.  Courses not described here may be used as advanced electives for the MS program upon receiving approval from the Chair of the Curriculum Committee and the Program Chair.

Biostatistics in Practice and the MS Thesis

All MS students must participate in the Biostatistics in Practice seminar and complete a Biostatistics in Practice project, which serves as the MS thesis.  The BIP project consists of a comprehensive analysis of a dataset and a report of the results. The BIP project may be completed in any semester.

Typical Course Sequence for Full-Time Students in the MS Program:

Transfer of Credit

Only courses considered at the graduate level may be transferred from previous training.  A maximum of 4 course units may be transfered from graduate work for MS and 8 for PhD. 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 beginning of each academic year, each student, in collaboration with his/her advisor, will prepare a proposal for the academic program including courses to be taken, courses to be transferred, and timelines for examinations and thesis preparation.

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Overview

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 6990) 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.

• Theory:

  • BSTA 6200: Probability (1 unit)
  • BSTA 6210: Statistical Inference I (1 unit)
  • BSTA 6220: Statistical Inference II (1 unit)

• Methods:

  • BSTA 6300: Statistical Methods and Data Analysis I (1 unit)
  • BSTA 6320: Statistical Methods for Categorical and Survival Data (Methods II) (1 unit)
  • BSTA 6510: Introduction to Linear Models & Generalized Linear Models (1 unit)
  • BSTA 6560: Longitudinal Data Analysis (0.5 unit)
  • BSTA 6600: Design of Observational Studies (0.5 unit)
  • BSTA 6610: Design of Interventional Studies I (0.5 unit)
  • BSTA 6700: Statistical Computing (1 unit)
  • BSTA 7540: Advanced Survival Analysis (0.5 unit)
  • BSTA 5110: 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 9990) 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 5300: Probability (1 unit)
• STAT 5310: Stochastic Processes (1 unit)
• STAT 9210: Observational Studies (1 unit)
• STAT 9250: Multivariate Analysis: Theory (1 unit) 
• OPIM 9300: Stochastic Models II (1 unit)
• BSTA 7510: Statistical Methods for Neuroimaging (1 unit)
• BSTA 7710: Applied Bayesian Analysis (1 unit)
• BSTA 7740: Statistical Methods for Evaluating Diagnostic Tests (0.5/1 unit) 
• BSTA 7750/STAT 9200: Sample Survey Methods (1 unit)
• BSTA 7820: Statistical Methods for Incomplete Data (1 unit)
• BSTA 7830: Multivariate and Functional Data Analysis (1 unit) 
• BSTA 7850: Statistical Methods for Genomic Data Analysis (1 unit) 
• BSTA 7860: Advanced Topics in Clinical Trials (1 unit)
• BSTA 7870: Methods for Statistical Genetics in Complex Human Disease (1 unit) 
• BSTA 7880: Functional Data Analysis (1 unit)
• BSTA 7890: Big Data (1 unit)
• BSTA 7900: Causal Inference in Biomedical Research (1 unit)
• BSTA 8200/STAT 5520: Statistical Inference III (1 unit)
• BSTA 8520/STAT 9100: Forecasting and Time Series (1 unit)
• BSTA 8540/STAT 9270: 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.

Minor

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 5090) 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 2020

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

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