Logan Brent R, Zhang Mei-Jie, Klein John P
Division of Biostatistics, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, Wisconsin 53226, USA.
Biometrics. 2011 Mar;67(1):1-7. doi: 10.1111/j.1541-0420.2010.01416.x.
Many time-to-event studies are complicated by the presence of competing risks and by nesting of individuals within a cluster, such as patients in the same center in a multicenter study. Several methods have been proposed for modeling the cumulative incidence function with independent observations. However, when subjects are clustered, one needs to account for the presence of a cluster effect either through frailty modeling of the hazard or subdistribution hazard, or by adjusting for the within-cluster correlation in a marginal model. We propose a method for modeling the marginal cumulative incidence function directly. We compute leave-one-out pseudo-observations from the cumulative incidence function at several time points. These are used in a generalized estimating equation to model the marginal cumulative incidence curve, and obtain consistent estimates of the model parameters. A sandwich variance estimator is derived to adjust for the within-cluster correlation. The method is easy to implement using standard software once the pseudovalues are obtained, and is a generalization of several existing models. Simulation studies show that the method works well to adjust the SE for the within-cluster correlation. We illustrate the method on a dataset looking at outcomes after bone marrow transplantation.
许多生存时间研究因存在竞争风险以及个体嵌套于群组中（如多中心研究中同一中心的患者）而变得复杂。针对具有独立观测值的累积发病率函数建模，已提出了几种方法。然而，当研究对象存在聚类时，需要通过对风险或子分布风险进行脆弱性建模，或者在边际模型中对聚类内相关性进行调整，来考虑聚类效应的存在。我们提出了一种直接对边际累积发病率函数进行建模的方法。我们在几个时间点从累积发病率函数计算留一法伪观测值。这些伪观测值用于广义估计方程中，以对边际累积发病率曲线进行建模，并获得模型参数的一致估计。我们推导了一个三明治方差估计量，以调整聚类内相关性。一旦获得伪值，该方法使用标准软件很容易实现，并且是对几种现有模型的推广。模拟研究表明，该方法能很好地针对聚类内相关性调整标准误。我们在一个观察骨髓移植后结局的数据集上展示了该方法。
