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. 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.
- Start date(s):
- Face-to-face: 8 June 2020 Online: 6 January 2020
- Time investment:
- Face-to-face: Five full working days Online: 5 weeks 9 hours per week
- University Medical Center Utrecht
- Faculty of Medicine
- Fee: This fee is exempt from VAT
- Face-to-face: € 830 Online: €785
- Extra information:
Both SPSS and R will be used during lectures and computer labs. While most techniques covered can be performed in SPSS, several require the use of R (or another package, such as Stata or SAS). Those unfamiliar with the (free) statistical package R are strongly encouraged to practice with it before beginning the course.
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.
By the end of the course, you should be able to:
- recognize or describe the type of problem addressed by a survival analysis
- define and recognize censored data
- define and interpret a survivor function and a hazard function, and describe their relation
- recognize the computer printout from a Cox proportional hazards model, a stratified Cox model, and a Cox model extended for time-dependent covariates
- state the meaning of the proportional hazards assumption and know how to check this assumption
- recognize which survival analysis technique is appropriate for a given research question and dataset
- interpret the computer printout for survival models, including hazard ratios, hypothesis testing, and confidence intervals