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.
Bibliographical noteFunding Information:
National Institutes of Health, R01DE024984, P30CA016059; National Research Foundation of Korea, 2019R1F1A1052239, 2019R1A4A1028, 2020R1A2C1A010115 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).
© 2021 John Wiley & Sons Ltd.
ASJC Scopus subject areas
- Statistics and Probability