Weighted Least-Squares Regression with Competing Risks Data

• Activity Number: 320 - Innovative Approaches for Modeling Time-to-Event Data in the Presence of Competing Risks and/or Time-Varying Covariates
• Type: Topic Contributed
• Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Dipankar Bandyopadhyay* and Sangbum Choi and Taehwa Choi and Hyunsoon Choi
• Companies: Virginia Commonwealth University and Korea University, Seoul, South Korea and Korea University and National Cancer Center, South Korea
• Keywords: competing risks; clustered; weighted least-square; AFT model; censoring; oncology

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. 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 talk, 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.