Abstract
The statistical analysis of survival time or failure time data is an important topic in many areas of research. In ordinary survival data, there is a single, possibly right-censored failure time for each individual. However, in a number of medical applications whereby data is collected an individual may experience one or more events but the first event will preclude the occurrence of another event under investigation. As a result, he/she can experience only one of several types of events. Such data are commonly referred to as competing risks data. Censoring due to such an event is generally not independent of the time to the event of interest.
This paper reviews statistical methods for analyzing a competing risks model. Both conceptual considerations and common approaches to one-sample inference; two sample comparison; and covariate effect modeling are discussed. The theory for the analysis of ordinary right-censored survival data can be applied under certain circumstances. Standard statistical software package can perform the necessary analysis, although interpretation of results will vary.
