Biostatistics II — Regression Analysis

Course Director: Yushu Shi, PhD

This course aims to introduce some common statistical methods and computational tools for predictive modeling, specifically regression analysis. Topics covered in this course include:

• Multivariable linear regression, variable selection, and model diagnosis
• Linear regression with variable transformation
• Generalized linear models, including Logistic regression model, Poisson regression model, variations of the Poisson model for zero-inflated data, multinomial Logistic regression model, and relevant model diagnoses
• Survival analysis: censoring mechanism, log-rank test, Kaplan-Meier curve, parametric survival models, including Cox model (and Fine and Gray model for competing risks data.)

Materials in parentheses are subject to students’ background, performance of past exams, and class progress.