ABSTRACT
In practice we are often interested in modeling the time to an event of interest. For example, in a longitudinal study some subjects may drop out before the end of the study, so one may be interested in finding any possible relationship between dropout times and covariates such as age and gender (e.g., are younger subjects more likely to drop out earlier?). Other common events of interest include time to death, time to infection of a disease, time to a car accident, time to completion of a task, etc. These types of data are called event-time data or survival data. The analysis of event-time data or survival data is called survival analysis. Survival data or event time data are very common in practice. For simplicity, in this chapter we will often treat the event as “death”, but the event can be defined in a much broader sense (e.g., dropout, infection, accident, etc.).
