Joint AFT random-effect modeling approach for clustered competing-risks data

Abstract

Competing risks data arise when occurrence of an event hinders observation of other types of events, and they are encountered in various research areas including biomedical research. These data have been usually analyzed using the hazard-based models, not survival times themselves. In this paper, we propose a joint accelerated failure time (AFT) modeling approach to model clustered competing risks data. Times to competing events are assumed to be log-linear with normal errors and correlated through a scaled random effect that follows a zero-mean normal distribution. Inference on the model parameters is based on the h-likelihood. Performance of the proposed method is evaluated through extensive simulation studies. The simulation results show that the estimated regression parameters are robust against the violation of the assumed parametric distributions. The proposed method is illustrated with three real competing risks data sets.

Keywords:

• Joint AFT model
• competing risks
• h-likelihood
• clustered data

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The research of Lin Hao was supported by the Science and Technology Project of Weifang University of Science and Technology [grant number KJRC2023012]. The research of Il Do Ha was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) [grant number NRF-2020R1F1A1A01056987]. The research of Youngjo Lee was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) [grant number 2019R1A2C1002408]. Jong-Hyeon Jeong's research was supported in part by the National Institute of Health (NIH) [grant numbers 5-U10-CA69974-09 and 5-U10-CA69651-11].