A positive stable frailty model for clustered failure time data with covariate-dependent frailty.
Summary In this article, we propose a positive stable shared frailty Cox model for clustered failure time data where the frailty distribution varies with cluster-level covariates. The proposed model accounts for covariate-dependent intracluster correlation and permits both conditional and marginal inferences. We obtain marginal inference directly from a marginal model, then use a stratified Cox-type pseudo-partial likelihood approach to estimate the regression coefficient for the frailty parameter. The proposed estimators are consistent and asymptotically normal and a consistent estimator of the covariance matrix is provided. Simulation studies show that the proposed estimation procedure is appropriate for practical use with a realistic number of clusters. Finally, we present an application of the proposed method to kidney transplantation data from the Scientific Registry of Transplant Recipients.
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA. dandanl@umich.edu
Dandan
D
Liu
John D
JD
Kalbfleisch
Douglas E
DE
Schaubel
D001699:Biometry / Q000379:methods*; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016030:Kidney Transplantation / Q000401:mortality*; D015233:Models, Statistical; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D016019:Survival Analysis; D015996:Survival Rate; D014481:United States / Q000453:epidemiology