Analysis of event times (also referred to as survival analysis) deals with data representing the time to a well-defined event. These data arise in engineering, economy, reliability, public health, biomedicine and other areas. One distinguishing feature of survival analysis is that it incorporates censored, truncated, and length-biased data. Another feature is the existence of time-dependent covariates. The main goals are to estimate the distribution of time-to-event for a group of individuals, to compare time-to-event among two or more groups, and to assess the relationship of covariates to event times. In multivariate survival analysis, one goal is the estimation of a multivariate distribution under censoring and/or truncation. In this setting, multi-state models are often used to represent the individual’s progress along time; important functions to estimate are the cause-specific hazard rate and distribution functions, the intensity functions, and the transition probabilities.
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