General regression model for the subdistribution of a competing risk under left-truncation and right-censoring
General regression model for the subdistribution of a competing risk under left-truncation and right-censoring
复制标题
DOI：
10.1093/biomet/asaa034
复制
发表时间：
2020-12-01
期刊：
影响因子：
2.7
通讯作者：
Fine, J. P.
中科院分区：
文献类型：
--
作者：
Bellach, A.;Kosorok, M. R.;Fine, J. P.
左截断给复杂的事件间隔时间数据的分析带来了额外的挑战。基于一种新的加权条件似然函数，提出了一种左截尾右删失竞争风险数据的一般半参数回归模型。针对次分布风险，我们的参数估计关于累积关联函数是直接可解释的。我们比较了最近文献中的不同权重，并从治愈模型的角度开发了基于伪风险集的启发式解释。我们的方法考虑了外部依赖于时间的协变量对次分布风险的影响。我们建立了估计的相合性和渐近正态，并提出了方差的夹心估计。在全面的仿真研究中，我们证明了所提出的方法具有良好的性能。比较三明治估计器和逆Fisher信息矩阵，我们观察到逆Fisher信息矩阵存在偏差，并且在左截断百分比较高的情况下覆盖概率减小。为了说明所提出的方法的实用性，我们研究了它在一个大型HIV疫苗效力试验数据集上的应用。
Left-truncation poses extra challenges for the analysis of complex time-to-event data. We propose a general semiparametric regression model for left-truncated and right-censored competing risks data that is based on a novel weighted conditional likelihood function. Targeting the subdistribution hazard, our parameter estimates are directly interpretable with regard to the cumulative incidence function. We compare different weights from recent literature and develop a heuristic interpretation from a cure model perspective that is based on pseudo risk sets. Our approach accommodates external time-dependent covariate effects on the subdistribution hazard. We establish consistency and asymptotic normality of the estimators and propose a sandwich estimator of the variance. In comprehensive simulation studies we demonstrate solid performance of the proposed method. Comparing the sandwich estimator with the inverse Fisher information matrix, we observe a bias for the inverse Fisher information matrix and diminished coverage probabilities in settings with a higher percentage of left-truncation. To illustrate the practical utility of the proposed method, we study its application to a large HIV vaccine efficacy trial dataset.