General regression model for the subdistribution of a competing risk under left-truncation and right-censoring. (17th June 2020)

• Record Type: Journal Article
• Authors: Bellach, A; Kosorok, M R; Gilbert, P B; Fine, J P
• Is Part Of: Biometrika. Volume 107:Number 4(2020:Dec.)
• Page Start: 949
• Page End: 964
• Publication Date: 2020-06-17
• Subjects: Cure model -- Fine–Gray model -- Proportional odds model -- Pseudo risk set -- Semiparametric transformation model -- Time-varying covariate -- Weighted conditional nonparametric maximum likelihood estimation; Biometry -- Periodicals; 570.1519505
• DOI: 10.1093/biomet/asaa034
• Languages: English
• ISSNs: 0006-3444

Abstract

Summary: 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.