AI 中文总结
针对半竞争风险数据中对非终端事件时间生存函数估计受终端事件相依删失影响的问题，提出条件copula图形估计器，可进行协变量调整，通过顺序迭代算法得到，用模拟和真实数据评估其性能并与无条件估计器比较。
AI 中文摘要
在半竞争风险数据中，关注的是对非终端事件时间生存函数的估计，它会受到终端事件的相依删失影响。该问题在文献中已被广泛研究，但大多集中在无条件情形。在许多临床应用中纳入协变量对于控制混杂和改进生存函数估计是必要的。本文提出一种条件copula图形估计器，它能在非终端和终端事件时间的边际生存函数及其相依结构中进行协变量调整。所提估计器是半参数的，条件copula用阿基米德copula参数化指定，但其相依参数函数和边际是非参数估计的。通过非终端事件生存函数和条件copula交替更新的顺序迭代算法得到该估计器。使用模拟和真实数据评估条件copula图形估计器的性能，并与无条件copula图形估计器比较，以研究未考虑协变量效应的后果。
英文摘要
In semi-competing risks data, the interest lies in the estimation of the survival function of a non-terminal event time, which is subject to dependent censoring by a terminal event. This problem has been extensively studied in the literature, but mostly focusing on unconditional settings. However, in many clinical applications incorporating covariates is necessary to control for confounding and improve survival function estimation. In this paper, we propose a conditional copula-graphic estimator that allows for covariate adjustment in the marginal survival functions of the non-terminal and terminal event times as well as in their dependence structure. The proposed estimator is semiparametric in that the conditional copula is specified parametrically using an Archimedean copula, but its dependence parameter function and margins are estimated nonparametrically. The estimator is obtained via a sequential iterative algorithm with alternating updates of the survival function of the non-terminal event and the conditional copula. The performance of the conditional copula-graphic estimator is assessed using simulated and real data, and is compared to that of the unconditional copula-graphic estimator to investigate the consequences of failing to account for covariate effects.
Comments18 pages, 3 figures, and supplemental material
