Modern Biostatistics Methodology I

4 credits · Graduate School

This course is the first in a two-course sequence on modern biostatistical methodology. Students will learn the statistical principles that govern analysis of data in biomedical research, focusing on principles of statistical inference and their application to data analysis techniques. Students will learn classical statistical theory and modern computational approaches, integrating mathematical derivations with applications toward evidence-based decision making. Topics include principles of probability; common distributions and their characteristics; bias and variability; introduction to large sample theory (e.g., the law of large numbers and the central limit theorem); estimation principles for means and proportions; an introduction to nonparametric statistics; Bayesian estimation with prior construction, conjugate families, and posterior-based inference; using simulation studies to evaluate operating characteristics; and computational approaches to inference including bootstrapping and permutation tests. 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 and serves as repeat credit for BIOS 6309. [4]