JSM 2005 - Toronto

Abstract #304839

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 323
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract - #304839
Title: A Bias Correction in Testing Treatment Efficacy under Informative Drop-out in Clinical Trials
Author(s): Fanhui Kong*+ and Yeh-Fong Chen and Kun Jin
Companies: U.S. Food and Drug Administration and U.S. Food and Drug Administration and U.S. Food and Drug Administration
Address: Division of Biometrics, Rockville, MD, 20852, United States
Keywords:
Abstract:

To test treatment efficacy in clinical trials, outcome variables often are measured repeatedly at scheduled visit times and treatment effects are tested at a fixed time. In such trials, patient dropout has been a major source of bias, especially when the dropout mechanism depends on the unobserved information. In this paper, for a shared parameter model of informative dropout, we improve the grouping method of Kong et al. (2004) to correct the biases caused by the missing information due to patient dropout. We improve this method by giving more accurate bias correction and a more accurate estimate of its standard deviation. In simulation studies, we compared the new method with the traditional last-observation-carried-forward (LOCF) analysis, the observed case (OC) analysis, and the mixed-model-repeated-measurement (MMRM) approach. The results indicated our method gave more accurate type-1 errors and more accurate coverage probabilities for estimating treatment efficacy.

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Revised March 2005