General regression model for the subdistribution of a competing risk under left-truncation and right-censoring

• 成果类型: Article
• 署名作者: Bellach, A.; Kosorok, M. R.; Gilbert, P. B.; Fine, J. P.
• 署名单位: University of Washington; University of Washington Seattle; University of North Carolina; University of North Carolina Chapel Hill; Fred Hutchinson Cancer Center
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asaa034
• 发表日期: 2020
• 页码: 949964
• 关键词: PROPORTIONAL HAZARDS MODEL; transformation models; ASYMPTOTIC THEORY

摘要

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.