Principles of Biostatistics II

4 credits · Graduate School

This course is the second in a two-course sequence on fundamental principles of biostatistics. Students will learn modern regression methods used to analyze biomedical and epidemiologic data, with an emphasis on practical model implementation and interpretation. Topics include simple and multiple linear regression, analysis of variance models, and introductory regression approaches for binary, ordinal, count, and time-to-event outcomes. Students will learn strategies for model building, evaluating confounding and effect modification, assessing model fit and assumptions using applied diagnostics, and communicating regression results effectively. Both frequentist and Bayesian approaches to regression modeling are covered, with emphasis on interpretation and applied use. Additional topics include flexible modeling with splines, basic approaches to model validation, and introductory methods for handling missing data. This course emphasizes application and interpretation, with theory introduced only as needed for understanding model behavior. This course is co-located with and serves as repeat credit for BIOS 6312. [4]