Kaplan-meier Analysis: a Practical Guide for Programmers

An important branch of statistics is survival analysis, which involves the modeling of time to event data. Within the context of clinical trials, this can represent the time between when a patient enrolls in a study and when a medically significant event occurs. Such analysis allows investigators to deduce, for example, the probability that an individual will survive past a certain time. A common problem in the analysis of clinical trials is how to appropriately consider censored data. The Kaplan-Meier (K-M) estimator (Kaplan, Meier 1985) of the survival function provides an elegant and robust method of survival analysis while properly handling censored data. Although it is common practice for SAS® programmers in the research community and pharmaceutical industry to implement the LIFETEST procedure to generate outputs, a comprehensive understanding of K-M survival analysis is required the appropriately interpret results. The objective of this presentation is to not only demonstrate the correct implementation of the LIFETEST procedure when studying survival data but also to describe the statistical fundamentals, the underlying calculations and the appropriate analytical tools so that the reader is well equipped to incorporate K-M analysis in their own research.