🤖 AI Summary
This study addresses the challenge of analyzing non-terminal event times in semi-competing risks data, where dependent censoring induced by terminal events and unadjusted covariates often leads to confounding bias. The authors propose a conditional Copula graphical estimation method that simultaneously incorporates covariates into both the marginal survival functions and the Copula dependence structure, enabling fully conditional modeling. Within a semiparametric framework, the approach employs Archimedean Copulas to capture covariate-dependent associations, estimates marginal distributions and dependence parameters nonparametrically, and solves the resulting system via an alternating iterative algorithm. Simulation studies and real-data analyses demonstrate that, compared to unconditional methods, the proposed approach substantially improves estimation accuracy, underscoring the critical role of covariate adjustment in semi-competing risks analysis.
📝 Abstract
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.