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Weighted least-squares regression with competing risks data

Authors
Choi, SangbumChoi, TaehwaCho, HyunsoonBandyopadhyay, Dipankar
Issue Date
30-1월-2022
Publisher
WILEY
Keywords
accelerated lifetime; clustered data; informative censoring; inverse probability weighting; subdistribution hazard; survival analysis
Citation
STATISTICS IN MEDICINE, v.41, no.2, pp.227 - 241
Indexed
SCIE
SCOPUS
Journal Title
STATISTICS IN MEDICINE
Volume
41
Number
2
Start Page
227
End Page
241
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/135249
DOI
10.1002/sim.9232
ISSN
0277-6715
Abstract
The semiparametric accelerated failure time (AFT) model linearly relates the logarithm of the failure time to a set of covariates, while leaving the error distribution unspecified. This model has been widely investigated in survival literature due to its simple interpretation and relationship with linear models. However, there has been much less focus on developing AFT-type linear regression methods for analyzing competing risks data, in which patients can potentially experience one of multiple failure causes. In this article, we propose a simple least-squares (LS) linear regression model for a cause-specific subdistribution function, where the conventional LS equation is modified to account for data incompleteness under competing risks. The proposed estimators are shown to be consistent and asymptotically normal with consistent estimation of the variance-covariance matrix. We further extend the proposed methodology to risk prediction and analysis under clustered competing risks scenario. Simulation studies suggest that the proposed method provides rapid and valid statistical inferences and predictions. Application of our method to two oncology datasets demonstrate its utility in routine clinical data analysis.
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