Liu Dandan, Kalbfleisch John D, Schaubel Douglas E
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA.
Biometrics. 2011 Mar;67(1):8-17. doi: 10.1111/j.1541-0420.2010.01444.x.
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.
摘要 在本文中，我们针对聚类失效时间数据提出了一种正稳定共享脆弱性Cox模型，其中脆弱性分布随聚类水平协变量而变化。所提出的模型考虑了协变量依赖的聚类内相关性，并允许进行条件推断和边际推断。我们直接从边际模型获得边际推断，然后使用分层Cox型伪偏似然方法来估计脆弱性参数的回归系数。所提出的估计量是一致的且渐近正态的，并提供了协方差矩阵的一致估计量。模拟研究表明，所提出的估计程序适用于具有实际聚类数量的实际应用。最后，我们将所提出的方法应用于来自移植受者科学登记处的肾移植数据。
