Department of Population Medicine, Harvard Medical School, Boston, MA, USA.
Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada.
Lifetime Data Anal. 2020 Jul;26(3):573-602. doi: 10.1007/s10985-019-09490-0. Epub 2019 Nov 15.
Motivated by a breast cancer research program, this paper is concerned with the joint survivor function of multiple event times when their observations are subject to informative censoring caused by a terminating event. We formulate the correlation of the multiple event times together with the time to the terminating event by an Archimedean copula to account for the informative censoring. Adapting the widely used two-stage procedure under a copula model, we propose an easy-to-implement pseudo-likelihood based procedure for estimating the model parameters. The approach yields a new estimator for the marginal distribution of a single event time with semicompeting-risks data. We conduct both asymptotics and simulation studies to examine the proposed approach in consistency, efficiency, and robustness. Data from the breast cancer program are employed to illustrate this research.
受乳腺癌研究计划的启发，本文研究了在终止事件导致信息性删失的情况下，多个事件时间的联合生存函数。我们通过阿基米德 Copula 将多个事件时间与终止事件的时间联系起来，以解释信息性删失。我们采用 Copula 模型下广泛使用的两阶段方法，提出了一种基于伪似然的易于实现的方法来估计模型参数。该方法为具有半竞争风险数据的单个事件时间的边缘分布提供了一个新的估计器。我们进行了渐近性和模拟研究，以检查所提出的方法在一致性、效率和稳健性方面的表现。乳腺癌计划的数据被用来举例说明这项研究。
