TY - JOUR
T1 - Weighted least-squares regression with competing risks data
AU - Choi, Sangbum
AU - Choi, Taehwa
AU - Cho, Hyunsoon
AU - Bandyopadhyay, Dipankar
N1 - Funding Information:
National Institutes of Health, R01DE024984, P30CA016059; National Research Foundation of Korea, 2019R1F1A1052239, 2019R1A4A1028, 2020R1A2C1A010115 Funding information
Funding Information:
The authors thank the anonymous associate editor and two reviewers, whose constructive comments led to a significantly improved presentation of our research. The effort of S. Choi was supported by the National Research Foundation (NRF) of Korea Grants (2019R1F1A1052239, 2019R1A4A1028) funded by the Korean Government. Dr. Cho also acknowledges NRF funding (2020R1A2C1A01011584), while Dr. Bandyopadhyay acknowledges funding from the United States National Institutes of Health (R01DE024984, P30CA016059).
Publisher Copyright:
© 2021 John Wiley & Sons Ltd.
PY - 2022/1/30
Y1 - 2022/1/30
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85117684552&partnerID=8YFLogxK
U2 - 10.1002/sim.9232
DO - 10.1002/sim.9232
M3 - Article
C2 - 34687055
AN - SCOPUS:85117684552
SN - 0277-6715
VL - 41
SP - 227
EP - 241
JO - Statistics in Medicine
JF - Statistics in Medicine
IS - 2
ER -