Modern Biostatistics Methodology II

4 credits · Graduate School

This course is the second in a two-course sequence on modern biostatistical methodology. Students will learn modern regression modeling and model-building techniques from both applied and theoretical perspectives. Topics include simple and multiple linear regression, analysis of variance models, and introductions to regression methods for binary, ordinal, count, and time-to-event outcomes. Mathematical and computational foundations of these models are integrated with applications, including matrix formulations of linear models, distributional assumptions, and properties of estimators. Both frequentist and Bayesian frameworks for regression modeling are covered, including conceptual foundations and their implications for inference. Additional topics include flexible modeling with splines, data-reduction techniques, model validation, and an introduction to methods for handling missing data. This course emphasizes implementation, interpretation, theory, and computation in approximately equal parts. This course is open to biostatistics students only and is co-located with BIOS 6310 Serves as repeat credit for BIOS 6310. [4]