Degree Requirements - PhD in Biostatistics Implementation and Prevention Science Methods Pathway

All courses are 1 unit unless otherwise noted.

The PhD degree requires a total of 16 course units. Course substitutions (other than those noted here) must be identified and approved by the student’s advisor, the Implementation Science Specialization Director and the DGS.

PhD Required Courses (12 course units)

• BIS 525 Seminar in Biostatistics and Journal Club - 0 units
• BIS 526 Seminar in Biostatistics and Journal Club - 0 units
• BIS 537 Statistical Methods for Causal Inference
• BIS 610 Applied Area Readings for Qualifying Exams
• BIS 623 Advanced Regression Analysis OR S&DS 612, Linear Models
• BIS 628 Longitudinal and Multilevel Data Analysis
• BIS 629 Advanced Methods in Implementation & Prevention Science
• BIS 631 Advanced Topics in Causal Inference
• BIS 643 Theory of Survival Analysis
• BIS 691 Theory of Generalized Linear Models
• BIS 699 Summer Internship in Biostatistical Research - 0 units
• EMD 533 Implementation Science
• EPH 508 Foundations of Epidemiology and Public Health
• EPH 600 Research Ethics and Responsibilities - 0 units
• EPH 608 Frontiers of Public Health**
• S&DS 610 Statistical Inference

*Students entering the program with an MPH or relevant graduate degree may be exempt from this requirement.

PhD Elective Courses (Choose at least 3 course units from the list below)

Implementation and Prevention Science is an interdisciplinary field. The more broadly you are trained, the more effective you will be as an independent statistical researcher as well as a collaborator.

• BIS 536 Measurement Error and Missing Data
• BIS 567 Bayesian Statistics
• BIS 646 Nonparametric Statistical Methods and Their Applications
• BIS 662 Computational Statistics CDE 516 Principles of Epidemiology II
• CDE 534 Applied Analytic Methods in Epidemiology
• EMD 538 Quantitative Methods for Infectious Disease Epidemiology
• HPM 570 Cost-Effectiveness Analysis and Decision Making ^
• HPM 575 Evaluation of Global Health Policies and Programs
• HPM 586 Microeconomics for Health Policy and Management
• HPM 587 Advanced Health Economics HPM 611 Policy Modeling ^
• S&DS 541 Probability Theory or Advanced Probability ^
• S&DS 565 Introductory Machine Learning OR S&DS 665 Intermediate Machine Learning (Alternate Yale courses in data mining & machine learning will be considered. Discuss with your advisor)
• S&DS 600 Advanced Probability
• SBS 541 Community Health Program Evaluation
• SBS 574 Developing a Health Promotion and Disease Prevention Intervention
• SBS 580 Qualitative Research Methods in Public Health ^

^ Strongly recommended for Implementation Science Specialization.

Course offerings subject to change.

In a number of courses, students gain actual experience with various aspects of research including preparation of a research grant, questionnaire design, preparation of a database for analysis, and analysis and interpretation of real data. In addition, doctoral students can gain research experience by working with faculty members on ongoing research studies prior to initiating dissertation research, which includes but is not limited to BIS 699. During the summer following each year of course work, candidates are required to take a research rotation that is approved by the department and communicated to the DGS.

The Department strives for doctoral dissertations that have a strong methodological component motivated by an important health question. Hence, the dissertation should include a methodological advance or a substantial modification of an existing method motivated by a set of data collected to address an important health question. The dissertation must also include the application of the proposed methodology to real data. Students that have chosen the Implementation and Prevention Science Methods pathway must complete a dissertation relevant to this topic. A fairly routine application of widely available statistical methodology is not acceptable as a dissertation topic. Candidates are expected not only to show a thorough knowledge of the posed health question, but also to demonstrate quantitative skills necessary for the creation and application of novel statistical tools.

Note: This pathway is relatively new and most students pursuing this are not yet in the dissertation phase

• New Statistical Methods for Mediation Analysis with Independent and Correlated Data
• Addressing Bias in Causal Effects Estimated Under Misspecified Interference Sets, With Application to HIV Prevention Trials

• Conduct independent research in the theory and/or application of biostatistics.
• Utilize critical thinking to select from a variety of analytical and computational tools to test statistical hypotheses, interpret results of statistical analyses and use these results to make relevant inferences from data.
• Demonstrate teaching and presentation skills to effectively communicate biostatistics theory and applications across a wide range of complex biomedical or public health problems.