Analyses of cumulative incidence functions via non-parametric multiple imputation

Authors

  • Ping K. Ruan,

    Corresponding author
    1. Center for Biostatistics in AIDS Research and Department of Biostatistics, Harvard University, 655 Huntington Ave., Boston, MA 02115, U.S.A.
    • Center for Biostatistics in AIDS Research and Department of Biostatistics, Harvard University, 655 Huntington Ave., Boston, MA 02115, U.S.A.
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  • Robert J. Gray

    1. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 44 Binney Str., Boston, MA 02115, U.S.A.
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Abstract

We describe a non-parametric multiple imputation method that recovers the missing potential censoring information from competing risks failure times for the analysis of cumulative incidence functions. The method can be applied in the settings of stratified analyses, time-varying covariates, weighted analysis of case-cohort samples and clustered survival data analysis, where no current available methods can be readily implemented. The method uses a Kaplan–Meier imputation method for the censoring times to form an imputed data set, so cumulative incidence can be analyzed using techniques and software developed for ordinary right censored survival data. We discuss the methodology and show from both simulations and real data examples that the method yields valid estimates and performs well. The method can be easily implemented via available software with a minor programming requirement (for the imputation step). It provides a practical, alternative analysis tool for otherwise complicated analyses of cumulative incidence of competing risks data. Copyright © 2008 John Wiley & Sons, Ltd.

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