PDF(Pubmed)
Abstract：
Multivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption that all event times are independent, and we provide a simple and stable EM-type algorithm. The resulting nonparametric maximum pseudolikelihood estimators for the regression parameters are shown to be consistent and asymptotically normal, with a limiting covariance matrix that can be consistently estimated by a sandwich estimator under arbitrary dependence structures for the related event times. We evaluate the performance of the proposed methods through extensive simulation studies and present an application to data from the Atherosclerosis Risk in Communities Study.
摘要：
当存在多种类型的事件或研究受试者集群时，会出现多变量间隔删失数据。使得事件时间潜在地相关,并且当每个事件仅已知在特定时间间隔内发生时。我们通过边际比例风险模型制定了潜在时变协变量对多变量事件时间的影响，同时未指定相关事件时间的依赖结构。我们在所有事件时间都是独立的工作假设下构造了非参数伪似然，我们提供了一个简单而稳定的EM型算法。所得到的回归参数的非参数最大伪似然估计量显示为一致且渐近正态，具有极限协方差矩阵，该矩阵可以在相关事件时间的任意依赖结构下通过三明治估计器进行一致估计。我们通过广泛的模拟研究来评估所提出方法的性能，并将其应用于社区动脉粥样硬化风险研究的数据。
