JSM 2005 - Toronto

Abstract #303017

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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 - #303017
Title: Including Adverse Event Data in Multiple Imputation of Efficacy Outcomes
Author(s): Shuyi Shen*+ and Craig Mallinckrodt and Xue Xin and Hua Deng and Ilya Lipkovich and Geert Molenberghs
Companies: Eli Lilly and Company and Eli Lilly and Company and Eli Lilly and Company and Eli Lilly and Company and Limburgs Universitair Centrum
Address: 7742 Newport Way D, Indianapolis, IN, 46256, United States
Keywords: Adverse Event ; Multiple Imputation ; Repeated measure
Abstract:

Clinical trials often assess change over time. However, valid analysis of longitudinal data can be problematic. Although the possibility of data missing not at random (MNAR) can never be ruled out, in highly controlled clinical trials, assuming data are missing at random (MAR) is often reasonable, especially compared to the very restrictive assumption of missing completely at random (MCAR). Maximum likelihood (ML) and multiple imputation (MI) are general analytic approaches valid under MAR that tend to yield comparable results when implemented in comparable ways. In either approach, it is possible to include auxiliary variables whose purpose is to improve the missing data procedure. Given the almost ever-present impact of adverse events on patient adherence, a logical starting point when choosing auxiliary variables is adverse event data. The present investigation used three imputation models to include adverse event data as an auxiliary variable in MI.

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Revised March 2005