Time-to-event and Longitudinal Data Analysis

Catalog description

This course will provide a comprehensive introduction to analysis of time to event and longitudinal data. Participants will learn how to handle censored data using Kaplan-Meier (KM) estimates, which model time-to-event outcomes. The Cox proportional hazards model will be explored to assess the impact of covariates on survival. For repeated measures, mixed-effects models will be used to account for individual variability over time. Generalized Estimating Equations (GEE) will be introduced as a method for analyzing correlated data in longitudinal studies. The course also covers practical strategies for handling missing data, ensuring robust and reliable analysis. By the end, participants will be equipped to analyze complex data structures in diverse research fields, including healthcare and social sciences.