A Positive Stable Frailty Model for Clustered Failure Time Data with Covariate‐Dependent Frailty |
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Authors: | Dandan Liu John D. Kalbfleisch Douglas E. Schaubel |
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Affiliation: | Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109‐2029, U.S.A. |
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Abstract: | 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. |
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Keywords: | Bridge distribution Clustered failure times Covariate‐dependent frailty Cox model Positive stable frailty Shared frailty |
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