The Kaplan Meier Estimate in Survival Analysis

The need for analyzing time to event data arises in a number of applied fields, such as epidemiology, public health and medicine [1]. ‘Time to event’ simply means the time from entry into a study until a subject has a particular outcome. The study that involves time to event can be a cohort study on a specific number of patients or participants which should be followed for a particular time period. In epidemiology, survival analysis is very important in the analysis of data involving patients/participants that should be followed to determine a particular event. Kaplan Meier estimate is best statistical method used in survival analysis to analyze the data and to make comparison between two groups of participants such as treatment group and control group using the log-rank test for hypothesis testing. In addition to medical disciplines, KaplanMeier analyses are also useful to other disciplines such as physics, engineering, economics, demography, among others. Example of Kaplan Meier estimate will be in the cohort study of lung cancer among smokers; here the selected number of smokers will be followed for 20 years. In this study, the Kaplan-Meier estimate will be used to determine or analyze the events, and censoring. Events here mean the development of the disease (the lung cancer) while censored are those who dropped out of the study or those who lost to follow-up. The fraction of smokers surviving the lung cancer will also be calculated using the survival table and KaplanMeier Estimate curve. Both the survival table and Kaplan-Meier estimate curve can be generated from the SPSS software or other statistical softwares such as Stata, SAS and R packages.