Weighted NPMLE for the Subdistribution of a Competing Risk
Research output: Contribution to journal › Journal article › Research › peer-review
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.
Original language | English |
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Journal | Journal of the American Statistical Association |
Volume | 114 |
Issue number | 525 |
Pages (from-to) | 259-270 |
ISSN | 0162-1459 |
DOIs | |
Publication status | Published - 2019 |
Links
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6502476/pdf/nihms914575.pdf
Accepted author manuscript
ID: 223256108