Survival data, or more generally, time-to-event data (where the “event” can be death, disease, recovery, relapse or another outcome), are frequently encountered in epidemiologic studies. Censoring is a problem characteristic to most survival data, and requires special data analytic techniques.
This course will give an introduction to survival analysis and cover many of the types of survival data and analysis techniques regularly encountered in epidemiologic research. The necessary statistical theory will be presented, but the course will focus on practical examples, with an emphasis on matching data analysis to the research question at hand. Lab sessions will give students the opportunity to apply the theory to real datasets.
Basic programming experience in R, e.g. the ability to read in data and run a simple linear model are assumed.
