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Survival Analysis Using Auxiliary Variables Via Multiple Imputation, with Application to AIDS Clinical Trial Data

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We develop an approach, based on multiple imputation, to using auxiliary variables to recover information from censored observations in survival analysis. We apply the approach to data from an AIDS clinical trial comparing ZDV and placebo, in which CD4 count is the time-dependent auxiliary variable. To facilitate imputation, a joint model is developed for the data, which includes a hierarchical change-point model for CD4 counts and a time-dependent proportional hazards model for the time to AIDS. Markov chain Monte Carlo methods are used to multiply impute event times for censored cases. The augmented data are then analyzed and the results combined using standard multiple-imputation techniques. A comparison of our multiple-imputation approach to simply analyzing the observed data indicates that multiple imputation leads to a small change in the estimated effect of ZDV and smaller estimated standard errors. A sensitivity analysis suggests that the qualitative findings are reproducible under a variety of imputation models. A simulation study indicates that improved efficiency over standard analyses and partial corrections for dependent censoring can result. An issue that arises with our approach, however, is whether the analysis of primary interest and the imputation model are compatible.
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Keywords: AIDS; Change-point model; Dependent censoring; Longitudinal data; Missing data; Random effects

Document Type: Research Article

Affiliations: 1: Department of Biostatistics, UCLA School of Public Health, 10833 Le Conte Avenue, Los Angeles, California 90095-1772, U.S.A. 2: Office of Research and Methodology, National Center for Health Statistics, 6525 Belcrest Road, Room 915, Hyattsville, Maryland 20782, U.S.A. 3: Department of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, Michigan 48109, U.S.A.

Publication date: 2002-03-01

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