Author(s)

Xuan Chen¹, Michael Baron^2,3

¹Department of Biostatistics and Programming, Sanofi, Beijing, China.
²Department of Mathematics and Statistics, American University, Washington DC, USA.
³Department of Mathematical Sciences, University of Texas at Dallas, Richardson, USA.

Effects of many medical procedures appear after a time lag, when a significant change occurs in subjects’ failure rate. This paper focuses on the detection and estimation of such changes which is important for the evaluation and comparison of treatments and prediction of their effects. Unlike the classical change-point model, measurements may still be identically distributed, and the change point is a parameter of their common survival function. Some of the classical change-point detection techniques can still be used but the results are different. Contrary to the classical model, the maximum likelihood estimator of a change point appears consistent, even in presence of nuisance parameters. However, a more efficient procedure can be derived from Kaplan-Meier estimation of the survival function followed by the least-squares estimation of the change point. Strong consistency of these estimation schemes is proved. The finite-sample properties are examined by a Monte Carlo study. Proposed methods are applied to a recent clinical trial of the treatment program for strong drug dependence.

Change-Point Problem, Failure Rate, Kaplan-Meier Estimation, Least Squares Estimation, Maximum Likelihood Estimation, Strong Consistency, Survival Function

Chen, X. and Baron, M. (2014) Change-Point Analysis of Survival Data with Application in Clinical Trials. Open Journal of Statistics, 4, 663-677. doi: 10.4236/ojs.2014.49062.