Survival Analysis (I)

Survival analysis is a collection of specialized methods used to analyze data in which time until an event occurs is the response variable of interest. The common objective of a survival analysis study is not only whether an event occurred, but also when it occurred. What is unique about survival analysis is that even if the subject did not experience an event, the subject's survival time is still taken into account. These observations are called censored observations and they can arise when a subject does not experience the event before the study ends, the subject is lost to follow-up during the study, or the subject withdraws from the study.

Usually, the first step in the analysis of survival data is to estimate and plot the survival function. One method to estimate the survival function is the Kaplan-Meier method that takes into account censored observations. Kaplan-Meier curves can be estimated and plotted using the LIFETEST procedure.

Another way to describe the distribution of survival times is to examine the hazard function. This function is essentially an instantaneous event rate that allows you to examine the forces of risk over time. The hazard function can be estimated using the life table method or by the SMOOTH macro developed by Allison (1995).