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Multiple augmentation for interval-censored data with measurement error

Authors

  • Xiao Song,

    Corresponding author
    1. Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, U.S.A.
    • Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Paul Coverdell Center, Room 129c, Athens, GA 30602, U.S.A.
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  • Shuangge Ma

    1. Department of Epidemiology and Public Health, Yale University, New Haven, CT, U.S.A.
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Abstract

There has been substantial effort devoted to the analysis of censored failure time with covariates that are subject to measurement error. Previous studies have focused on right-censored survival data, but interval-censored survival data with covariate measurement error are yet to be investigated. Our study is partly motivated by analysis of the HIV clinical trial AIDS Clinical Trial Group (ACTG) 175 data, where the occurrence time of AIDS is interval censored and the covariate CD4 count is subject to measurement error. We assume that the data are realized from a proportional hazards model. A multiple augmentation approach is proposed to convert interval-censored data to right-censored data, and the conditional score approach is then employed to account for measurement error. The proposed approach is easy to implement and can be readily extended to other semiparametric models. Extensive simulations show that the proposed approach has satisfactory finite-sample performance. The ACTG 175 data are then analyzed. Copyright © 2007 John Wiley & Sons, Ltd.

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