Title | Weighted NPMLE for the Subdistribution of a Competing Risk. |
Publication Type | Journal Article |
Year of Publication | 2019 |
Authors | Bellach, Anna, Michael R. Kosorok, Ludger Rüschendorf, and Jason P. Fine |
Journal | J Am Stat Assoc |
Volume | 114 |
Issue | 525 |
Pagination | 259-270 |
Date Published | 2019 |
ISSN | 0162-1459 |
Abstract | 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. |
DOI | 10.1080/01621459.2017.1401540 |
Alternate Journal | J Am Stat Assoc |
Original Publication | Weighted NPMLE for the subdistribution of a competing risk. |
PubMed ID | 31073256 |
PubMed Central ID | PMC6502476 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States U24 CA076518 / CA / NCI NIH HHS / United States |
Weighted NPMLE for the Subdistribution of a Competing Risk.
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