JSM 2005 - Toronto

Abstract #302877

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 349
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract - #302877
Title: Robust Analysis of Incomplete Longitudinal Data in Clinical Trials
Author(s): Devan Mehrotra*+ and Robin Mogg
Companies: Merck Research Laboratories and Merck Research Laboratories
Address: Clinical Biostatistics, UN-A102, Blue Bell, PA, 19422, United States
Keywords: antiretroviral immunotherapy ; HIV vaccine ; missing at random ; multiple imputation ; rank-based test ; Wei-Lachin test
Abstract:

In a typical longitudinal comparative clinical trial, some subjects drop out before the end of their planned follow-up period. The resulting incomplete longitudinal data are commonly analyzed using single imputation-based, last-observation-carried-forward, "worst-rank" score methods---likelihood-based methods (e.g., REML, the default in SAS PROC MIXED) that assume multivariate normality and a missing at random (MAR) dropout mechanism. Distribution-free tests for incomplete longitudinal data also have been developed, although under the stronger assumption that data are missing completely at random (MCAR). We propose an alternate method of analysis that combines multiple imputation of missing values with the Wei-Lachin (1984) distribution-free method. The advantages of our proposed robust method over standard methods in the presence of random drop-out and when parametric assumptions fail are quantified via simulation. A randomized antiretroviral immunotherapy clinical trial is used to motivate the problem.

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Revised March 2005