Semiparametric inferences for association with semi-competing risks data


  • Debashis Ghosh

    Corresponding author
    1. Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48105, U.S.A.
    • Department of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Room M4057, Ann Arbor, MI 48109-2029, U.S.A.
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In many biomedical studies, it is of interest to assess dependence between bivariate failure time data. We focus here on a special type of such data, referred to as semi-competing risks data. In this article, we develop methods for making inferences regarding dependence of semi-competing risks data across strata of a discrete covariate Z. A class of rank statistics for testing constancy of association across strata are proposed; its asymptotic properties are also derived. We develop a novel resampling-based technique for calculating the variances of the proposed test statistics. In addition, we develop methods for combining test statistics for assessing marginal effects of Z on the dependent censoring variable as well as its effects on association. The finite-sample properties of the proposed methodology are assessed using simulation studies, and they are applied to data from a leukaemia transplantation study. Copyright © 2005 John Wiley & Sons, Ltd.