Ph.D. in Biostatistics

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Below is the academic and application information on the Ph.D. in Biostatistics program. If you are considering a career in Biostatistics or in applying to the program, please visit Why Study Biostatistics and Student and Alumni Spotlights for more information. 

The Ph.D. in Biostatistics, offered through the Division of Biostatistics in the Department of Public Health Sciences at the Miller School of Medicine, provides a flexible curriculum to cover the basics.

Depending on their background, a student will follow either a Track A and Track B stream to completion of the Ph.D. All students will be required to take elective courses referred to as cognates which will be linked in a substantive way to the Ph.D. dissertation. See below for details.

The Doctoral degree in Biostatistics degree is accredited by the Council on Education for Public Health (CEPH).

Upon completion of the Doctoral degree in Biostatistics, all graduates will be able to:

  • Competencies:
    Describe the core disciplines of public health and how they apply to improving population health
    Apply epidemiologic methods to the measurement and study of population health and the prevention of infectious and chronic disease
    Develop novel statistical methodology
    • - identify the limitations of existing methodology and standard techniques for adapting existing methodology to new epidemiological and public health settings
    • - identify epidemiology and public health settings where techniques beyond modification of existing techniques are necessary (i.e. new methodology)
    • - use advanced theory and computation to develop new methodology for addressing research problems in epidemiology and public health
    Analyze complex data from epidemiology, public health and other biomedical settings with subject matter collaborators using cutting edge statistical techniques
    • - read, understand and use the most recent literature in epidemiology, public health, and other biomedical settings relevant to an analytic task
    • - formulate a plan for data gathering, data management and statistical analysis
    • - carry out plan effectively to answer questions of substantive interest in epidemiology, public health, and other biomedical settings
    Communicate findings verbally (with collaborators and at conferences), and in writing (journal articles)
    • - prepare a seminar for presentation to either substantive (epidemiology, public health, and other biomedical ) or statistical audiences
    • - develop the elusive skill of learning a substantive application rapidly by listening effectively to the subject matter specialist and asking pertinent questions as needed
    • - integrate statistical concepts into presentations for public health and epidemiology audiences in forms that they will find useful and intelligible
    • - prepare clear reports, analysis plans for grant applications and statistical sections of journal articles
    Demonstrate cognate field expertise
    • - develop enough knowledge in a specific subject domain within the epidemiology, public health, or other area of biomedical science to communicate easily with scientists in that domain
    • - use this background to formulate new biostatistical problems to solve (the dissertation is the primary example of this)
    Teach graduate students from public health and from statistics and biostatistics
    • - learn how to organize a large sequence of lectures so that they follow logically and provide an overview of a topic area in statistics or biostatistics
    • - combine diverse teaching techniques and materials so that students with different learning styles can master the material
    Recognize potential ethical issues and implement the concepts of ethical conduct of research
  • Total required credits:  67 credits


All students are required to take a minimum of four 3-credit graduate courses in specific topics referred to as cognates which will be related in a substantive way to the Ph.D. dissertation. Extra criteria requiring courses closely related to the student’s thesis work, or that a member in a subject matter discipline be on the student’s advisory committee, may apply in some cases. The cognate requirement will enable students to produce a biostatistically-sophisticated Ph.D. thesis and provide outstanding opportunities for graduates.

Track A: Students who meet prerequisite requirements

(1) A minimum of three semesters of calculus, including partial derivatives and techniques for solving multiple integrals, (2) One semester of linear algebra, (3) One semester of probability theory, (4) Four additional courses in statistics or biostatistics. The four courses are to include a general introduction, linear regression, introductory mathematical statistics and at least one more course (commonly drawn from survey sampling, multivariate, time series, nonparametric, etc.), and (5) At least two additional courses in statistics, biostatistics or related fields.

Track B: Do not meet all prerequisite requirements

During the first year, students are expected to make up any deficiencies. This will be decided on a case-by-case basis by the graduate program director.

Ph.D. in Biostatistics Curriculum 

The total requirements for the Ph.D. in Biostatistics are 67 credits. The credits must be completed as core, dissertation, and elective courses to meet the requirements. 

  • Core Courses (37 credits)
    BST 610 Introduction to Statistical Collaboration (3 credits)
    MTH 624 Introduction to Probability Theory (3 credits)
    MTH 625 Introduction to Mathematical Statistics (3 credits)
    BST 630 Longitudinal and Multilevel Data (3 credits)
    BST 640 Modern Numerical Multivariate Methods (3 credits)
    MTH 642 Statistical Analysis (3 credits)
    BST 650 Topics in Biostatistical Research (1 credit x 4 semesters)
    BST 665 Design and Analysis of Clinical Trials (3 credits)
    BST 676 Introduction to Generalized Linear Models (3 credits)
    BST 680 Advanced Statistical Theory (3 credits)
    BST 690 Theory of Survival Analysis (3 credits)
    BST 691 High Dimensional and Complex Data (3 credits)
  • Dissertation (12 credits)
    BST 830 Doctoral Dissertation (pre-candidacy) (1-12 credit hours)
    BST 840 Doctoral Dissertation (Post-Candidacy) (1-12 credit hours)
  • Electives (12 credits)
    The following are examples of electives(some of which students have taken in the past). This is not meant to be an exhaustive list, but rather illustrates the scope of available electives.
    CSC 548 BioInformatics Algorithms (3 credits)
    ECO 630 Advanced Econometrics (3 credits)
    EPH 711 Cancer Epidemiology (3 credits)
    EPH 740 Basic Pathology (3 credits)
    HGG 630 Variation and Disease (2 credits)
    HGG 640 Family Studies and Genetic Analysis (2 credits)
    BST 649 Advanced Independent Study (1-3 credits)
    BST 670 Bayesian Analysis: Concepts, Theory, and Computing (3 credits)


A written diagnostic exam will be given at the end of the first year to ensure the student has made up deficiencies and is making adequate progress. The examination covers basic foundational material every graduate should have thoroughly assimilated. Students who perform poorly on the exam are required to demonstrate their mastery of the material in some other way, which is handled on a case-by-case basis.

A second oral and written exam will be administered at the end of the third year. Once a student passes the second exam, they will formally become a Ph.D. candidate.

For curriculum information and course descriptions, please click on the right. For the program application, click below.