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.
Degree Requirements - PhD in Biostatistics Standard Pathway
Required Courses (10 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 - 1 unit
- BIS 623 Advanced Regression Analysis or S&DS 612, Linear Models - 1 unit
- BIS 628 Longitudinal and Multilevel Data Analysis - 1 unit
- BIS 643 Theory of Survival Analysis - 1 unit
- BIS 678 Statistical Practice I - 1 unit
- BIS 681 Statistical Practice II - 1 unit
- BIS 691 Theory of Generalized Linear Models - 1 unit
- BIS 695 Summer Internship in Biostatistical Research - 0 units
- BIS 508 Foundations of Epidemiology and Public Health - 1 unit
- EPH 600 Research Ethics and Responsibilities - 0 units
- EPH 608 Frontiers of Public Health* - 1 unit
- S&DS 610 Statistical Inference - 1 unit
PhD Elective Courses (6 course units)
* Students entering the program with an MPH or relevant graduate degree may be exempt from this requirement.
Course offerings subject to change.
In a number of courses, especially the Statistical Consulting (BIS 578a) 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 695c. 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.
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