Multiple Survival Outcomes and Competing Risks
Until now the type of survival data we have considered has, as an endpoint, a single cause of death, and the survival times of each case have been assumed to be independent. Methods for analyzing such survival data will not be sufficient if cases are not independent or if the event is something that can occur repeatedly. An example of the first type would be clustered data. For instance, one might be interested in survival times of individuals that are in the same family or in the same unit, such as a town or school. In this case, genetic or environmental factors mean that survival times within a cluster are more similar to each other than to those from other clusters, so that the independence assumption no longer holds. In the second case, if the event of interest is, for example, the occurrence of a seizure, the event may repeat indefinitely. Then we would have multiple times per person. Special methods are needed to handle these types of data structures, which we shall discuss in Sect. 9.1. A different situation arises when only the first of several outcomes is observable, a topic we will take up in Sect. 9.2.
KeywordsProstate Cancer Hazard Function BRCA Mutation Carrier Frailty Model Cumulative Incidence Function
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