Analysing multicentre competing risks data with a mixed proportional hazards model for the subdistribution

Abstract

In the competing-risks setting, to test the effect of a covariate on the probability of one particular cause of failure, the Fine and Gray model for the subdistribution hazard can be used. However, sometimes, competing risks data cannot be considered as independent because of a clustered design, for instance in registry cohorts or multicentre clinical trials. Frailty models have been shown useful to analyse such clustered data in a classical survival setting, where only one risk acts on the population. Inclusion of random effects in the subdistribution hazard has not been assessed yet. In this work, we propose a frailty model for the subdistribution hazard. This allows first to assess the heterogeneity across clusters, then to incorporate such an effect when testing the effect of a covariate of interest. Based on simulation study, the effect of the presence of heterogeneity on testing for covariate effects was studied. Finally, the model was illustrated on a data set from a registry cohort of patients with acute myeloid leukaemia who underwent bone marrow transplantation.