This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 345
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract - #308222
Title: Change-Point Analysis of Survival Data with Application in Clinical Trials
Author(s): Xuan Chen*+ and Michael Baron
Companies: sanofi pasteur and The University of Texas at Dallas
Address: , , International, 100022, China
Keywords: Change-point problem ; survival function ; failure rate ; maximum likelihood estimation ; lease squares estimation ; strong consistency

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.

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