Zhang Hui, Schaubel Douglas E, Kalbfleisch John D
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA.
Biometrics. 2011 Mar;67(1):18-28. doi: 10.1111/j.1541-0420.2010.01445.x.
Case-cohort sampling is a commonly used and efficient method for studying large cohorts. Most existing methods of analysis for case-cohort data have concerned the analysis of univariate failure time data. However, clustered failure time data are commonly encountered in public health studies. For example, patients treated at the same center are unlikely to be independent. In this article, we consider methods based on estimating equations for case-cohort designs for clustered failure time data. We assume a marginal hazards model, with a common baseline hazard and common regression coefficient across clusters. The proposed estimators of the regression parameter and cumulative baseline hazard are shown to be consistent and asymptotically normal, and consistent estimators of the asymptotic covariance matrices are derived. The regression parameter estimator is easily computed using any standard Cox regression software that allows for offset terms. The proposed estimators are investigated in simulation studies, and demonstrated empirically to have increased efficiency relative to some existing methods. The proposed methods are applied to a study of mortality among Canadian dialysis patients.
病例队列抽样是研究大型队列常用且有效的方法。现有的大多数病例队列数据分析方法都涉及单变量失效时间数据的分析。然而，在公共卫生研究中经常会遇到聚类失效时间数据。例如，在同一中心接受治疗的患者不太可能是独立的。在本文中，我们考虑基于估计方程的方法来处理聚类失效时间数据的病例队列设计。我们假设一个边际风险模型，在各聚类中有共同的基线风险和共同的回归系数。所提出的回归参数和累积基线风险的估计量被证明是一致的且渐近正态的，并且推导了渐近协方差矩阵的一致估计量。使用任何允许偏移项的标准Cox回归软件都可以轻松计算回归参数估计量。在模拟研究中对所提出的估计量进行了研究，并通过实证证明相对于一些现有方法具有更高的效率。所提出的方法应用于加拿大透析患者死亡率的研究。
