title: Competing risks regression for clustered data.
abstract: A population average regression model is proposed to assess the marginal effects of covariates on the cumulative incidence function when there is dependence across individuals within a cluster in the competing risks setting. This method extends the Fine-Gray proportional hazards model for the subdistribution to situations, where individuals within a cluster may be correlated due to unobserved shared factors. Estimators of the regression parameters in the marginal model are developed under an independence working assumption where the correlation across individuals within a cluster is completely unspecified. The estimators are consistent and asymptotically normal, and variance estimation may be achieved without specifying the form of the dependence across individuals. A simulation study evidences that the inferential procedures perform well with realistic sample sizes. The practical utility of the methods is illustrated with data from the European Bone Marrow Transplant Registry.
authors: Division of Biostatistics, School of Public Health, Yale University, New Haven, CT 06520, USA. bingqing.zhou@yale.edu
authors: Bingqing
authors: B
authors: Zhou
authors: Jason
authors: J
authors: Fine
authors: Aurelien
authors: A
authors: Latouche
authors: Myriam
authors: M
authors: Labopin
mesh_terms: D016026:Bone Marrow Transplantation; D016000:Cluster Analysis; D003198:Computer Simulation; D006087:Graft vs Host Reaction; D006801:Humans; D015994:Incidence; D015470:Leukemia, Myeloid, Acute / Q000628:therapy; D015233:Models, Statistical; D012307:Risk Factors