Weighted NPMLE for the Subdistribution of a Competing Risk.

TitleWeighted NPMLE for the Subdistribution of a Competing Risk.
Publication TypeJournal Article
Year of Publication2019
AuthorsBellach, Anna, Michael R. Kosorok, Ludger Rüschendorf, and Jason P. Fine
JournalJ Am Stat Assoc
Date Published2019

Direct regression modeling of the subdistribution has become popular for analyzing data with multiple, competing event types. All general approaches so far are based on non-likelihood based procedures and target covariate effects on the subdistribution. We introduce a novel weighted likelihood function that allows for a direct extension of the Fine-Gray model to a broad class of semiparametric regression models. The model accommodates time-dependent covariate effects on the subdistribution hazard. To motivate the proposed likelihood method, we derive standard nonparametric estimators and discuss a new interpretation based on pseudo risk sets. We establish consistency and asymptotic normality of the estimators and propose a sandwich estimator of the variance. In comprehensive simulation studies we demonstrate the solid performance of the weighted NPMLE in the presence of independent right censoring. We provide an application to a very large bone marrow transplant dataset, thereby illustrating its practical utility.

Alternate JournalJ Am Stat Assoc
Original PublicationWeighted NPMLE for the subdistribution of a competing risk.
PubMed ID31073256
PubMed Central IDPMC6502476
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
U24 CA076518 / CA / NCI NIH HHS / United States