Change-Point Analysis of Survival Data with Application in Clinical Trials

• Activity Number: 345
• Type: Contributed
• Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Xuan Chen*+ and Michael Baron
• Companies: sanofi pasteur and The University of Texas at Dallas
• Keywords: Change-point problem ; survival function ; failure rate ; maximum likelihood estimation ; lease squares estimation ; strong consistency

Abstract

Effects of many medical procedures appear after a time lag, when a significant change occurs in subjects' failure rate. This paper focuses on the estimation of such changes. 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 estimator of the survival function followed by the least squares estimation of the change point, which is strongly consistent. Proposed methods are applied to a recent clinical trial of the treatment program for strong drug dependence.