# Penalized Variable Selection for Joint AFT Random‐Effect Model With Clustered Competing‐Risks Data

## TL;DR

This paper introduces a new variable selection method for analyzing clustered competing-risks data using a joint AFT random-effect model, showing better performance than existing methods.

## Contribution

A novel penalized h-likelihood variable selection method for fixed effects in joint AFT competing-risk models is proposed.

## Key findings

- Penalized methods like SCAD and HL outperform LASSO in variable selection for the joint AFT model.
- The proposed method was validated using simulation studies and real clinical datasets.
- The approach improves the analysis of clustered competing-risks data by accounting for random effects.

## Abstract

Clustered competing‐risks data often arise in clinical studies, such as multi‐center clinical trials, where the occurrence of an event within a cluster hinders the observation of other types of events. The correlation resulting from clustering can be modeled using random effects. These competing‐risks data have usually been analyzed using hazard‐based models, rather than survival times themselves. Hao et al. proposed a cause‐specific joint accelerated failure time (AFT) random‐effect modeling approach for analyzing the clustered competing‐risks data, which is easy to interpret. In this article, we propose a variable selection method for fixed effects using a penalized h‐likelihood (HL) procedure in the joint AFT competing‐risk model. Simulation studies were conducted to evaluate the performance of the proposed variable selection procedure, which concluded that the penalized methods of SCAD and HL are more appropriate than that of LASSO. The proposed method is illustrated with two real clinical datasets.