At the end of the course, the student 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
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Period (from-till): This course is taught several times per year, during fulltime weekdays according to the following Schedule (Face-2-Face in Period A, Online in Period B):
| Education form |
Startdate |
Enddate |
Registration period |
| Face-2-Face |
27-05-2024 |
31-05-2024 |
BMS_P4_A |
| Onilne* |
08-01-2024 |
09-02-2024 |
BMS_P2_B |
*Online courses are only available for Epidemiology students following a full online programme.
Contact details: Educational Office Epidemiology
E-mail: msc-epidemiology@umcutrecht.nl
Registration:
Single courses — MSc Epidemiology (msc-epidemiology.nl)
Course coordinator:
Rebecca Stellato
Course description:
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.
Note: 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.
Literature/study material used:
-
Mandatory for students in own Master’s programme:
MIght be for a specialization programme of Epidemiology & Epidemiology Postgraduate
Optional for students in other GSLS Master’s programme:
Yes
Prerequisite knowledge:
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 is required!.
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