Semi-parametric inferences for association with semi-competing risks data.
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 re-sampling-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.
Ghosh
Debashis
D
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48105, USA. ghoshd@umich.edu
D016026:Bone Marrow Transplantation / Q000401:mortality*; D003198:Computer Simulation; D006801:Humans; D007938:Leukemia / Q000401:mortality* / Q000628:therapy; D015233:Models, Statistical; D015999:Multivariate Analysis; D018570:Risk Assessment / Q000379:methods; D015996:Survival Rate