Students choose a topic on their own under the guidance of a faculty adviser who will then grade the final written report

This course will introduce students to both statistical theory and practice of causal inference. We will review the basics of causal inference, introduce a missing data perspective of causal inference and instrumental variable methods. We then cover 3 advanced topics based on a survey to students. Tentative topics include randomization inference, mediation analysis, principal stratification, measurement error, natural experiments, and causal inference with interference.

Major designs used in clinical investigations; alternative approaches to the analysis of gathered data. Prerequisite: BIOST&EP 640.

Integration of linear models with experimental design and sampling, considering applications with unbalanced and missing data. In-depth discussion of mixed models including parameterizations, analysis of covariance, unequal numbers of observations per cell, missing cells. Repeated measure designs and longitudinal data analysis emphasized, with many examples illustrated using SAS. Prerequisite: BIOST&EP 640 and 691F or equivalent. Recommended: Bioepi 741 and 744.

This is not available at this time

An introduction to data management for research projects in the biomedical sciences using microcomputers. Topics include design of data collection forms, data entry, computer managed documentation and statistical computing using SAS.

The course introduces advanced central topics in biostatistics and health data science including survival analysis, design and analysis of clinical trials, models for correlated data, bayesian modeling, and causal inference. The course motivates statistical reasoning and methods through substantive research questions and features of data typically available in public health and biomedical research. Students will obtain hands-on experience in applying selected methods on real data using the statistical programming language R.