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Semiparametric inferences for association with semi‐competing risks data
Author(s) -
Ghosh Debashis
Publication year - 2005
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2327
Subject(s) - resampling , covariate , bivariate analysis , censoring (clinical trials) , statistics , econometrics , computer science , inference , statistical hypothesis testing , sample size determination , mathematics , artificial intelligence
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.