Is survival the fittest? A post hoc evaluation of event history estimations in an experimental design

Survival analysis has become one of the most common statistical techniques for analyzing longitudinal data in the social sciences, largely due to its ability to produce unbiased estimates in the presence of censored or incomplete data. A follow-up case record review of subjects (N = 175) in a recent experimental study provides the opportunity to revisit original survival estimates and compare them to estimates generated using nearly complete data. Results indicate that Kaplan–Meier median estimates of survival time and parameter estimates using Cox Proportional Hazards Regression were relatively accurate despite differential censoring between the treatment and control groups. Estimated mean averages tended to be inaccurate in the presence of substantial censoring. Implications for research and practice are discussed.