Survival data, or more generally, time-to-event data (where the “event” can be death,  disease, recovery, relapse or another outcome), is 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

At the end of the course, the student should be able to:

1. recognize or describe the type of problem addressed by a survival analysis
2. define and recognize censored data
3. define and interpret a survivor function and a hazard function, and describe their relation
4. recognize the computer printout from a Cox proportional hazards model, a stratified Cox model, and a Cox model extended for time-dependent covariates
5. state the meaning of the proportional hazards assumption and know how to check this assumption
6. recognize which survival analysis technique is appropriate for a given research question and dataset
7. interpret the computer printout for survival models, including hazard ratios, hypothesis testing, and confidence intervals

At least one course in basic statistical methods, up to and including simple and multiple linear regression, such as: Classical Methods in Data Analysis, Introduction to Biostatistics for Researchers, or their equivalent.
Familiarity with the statistical package R isrequired!

This course can also be followed online. Look at the website http://elevatehealth.eu/courses for more information and costs.