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Survival analysis was originally developed in medical statistics, to analyse the effects of covariates on survival times (hence its name). The purpose of such analyses is for instance to find out whether certain treatments reduce the incidence of death. The methods are especially designed to take so-called “censored data” into account, when death occurs due to other causes than the studied disease or patients survive until the end of the study. Techniques from survival analysis can be applied in a variety of biological contexts, whenever data consist of time until occurrence of a certain event. Examples are many types of behavioural data, times until recapture, and latency data.
The course consists of one full day during which we will introduce the concept of survival analysis and show how to apply the methods to biological data. Main subjects are how to handle censored data, estimation of Kaplan-Meier survivor curves, the Log-Rank test for testing differences between survival times, and Cox' regression model for estimating and testing effects of covariates. Lectures will be alternated with time for practice, where participants apply the methods with either SAS or R.
|Course Frequency||Once every two or three years|
|Level||Basic statistical knowledge is required|
|Intended credits||0.3 ECTS|
|Course organisation||The C.T. de Wit Graduate School for Production Ecology and Resource Conservation (PE&RC)|
|More information||Course website|