The YSPH Biostatistics Program offers me great flexibility and opportunities to explore my research interests and collaborate with interdisciplinary laboratories locally and globally. The collaborative, supportive and diverse environment allows me to learn, grow and develop both professional and personal skills.

Wei Dai

PhD Candidate

Degree Requirements - PhD in Biostatistics Standard Pathway

2023-24 Matriculation

All courses are 1 unit unless otherwise noted.

The Ph.D. 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 and the DGS.

Required Courses (8 course units)

BIS 525 Seminar in Biostatistics and Journal Club - 0 units
BIS 526 Seminar in Biostatistics and Journal Club - 0 units
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 643 Theory of Survival Analysis
BIS 691 Theory of Generalized Linear Models
BIS 699 Summer Internship in Biostatistical Research - 0 units
BIS 508 Foundations of Epidemiology and Public Health
EPH 600 Research Ethics and Responsibilities - 0 units
EPH 608 Frontiers of Public Health (not offered in 2023-24)*
S&DS 610 Statistical Inference

PhD Elective Courses (8 course units)

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

Course offerings subject to change.

rev. 7.3.2023

Research Experience

In a number of courses, especially the Statistical Consulting (BIS 678) course 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 Dissertation

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.

Recent Dissertation Projects

Causal Inference for Intervention Effects Under Contagion
Statistical Methods for Identifying Shared Genetic Architecture and Genetic Risk Factors in Lung Diseases
Single Cell and Multi-Omics Data Integration Computational Methodologies
Causal Inference for Time-Varying Treatments for Hypertension
Ancestry-Specific Genetic and Epigenetic Association Studies of Smoking Initiation and Cessation in Admixed Populations
Novel Methods for Identification and Inference in Public Health
Genetic Covariance Analysis Reveals Heterogeneous Etiologic Sharing of Complex Traits: From Theory to Applications
Latent Space Construction for Analyzing Large Genomic Data Sets
Leveraging Genomics and Immunomics for More Precise Immunotherapy
Statistical Methods for Identifying Gene Experession and Epigenetic Signatures in Post-Traumatic Stress Disorder (PTSD)
Functional Connectivity to Link Genes to Behavior in the Human Brain