Comparison of statistical and machine learning methods in competing risk analysis

Survival analysis is widely used to model event times and plays a crucial role in various fields such as medicine, life sciences, and engineering.However, conventional survival models often focus on single-event scenarios, making them inadequate when multiple events exist.To address this limitation, competing risks models have been introduced and have gained increasing attention in recent studies.Despite this progress, research that simultaneously considers cluster effects, model misspecification, and informative censoring in competing risks settings remains limited.In this study, we compare various survival models, including the Fine and Gray (FG) model, Cause-Specific Hazard (CSH) model, Event-Free Survival (EFS) model, Random Survival Forest (RSF), Cox regression with frailty (CF), Mixed Effect Cox model (CoxME), Competing Risks Regression for Stratified and Clustered Data (CRRSC), penalized regression models (LASSO, MCP, SCAD), boosting methods (mboost, CoxBoost), and Direct Binomial Regression (DBR).Through simulation studies, we evaluate model performance under varying cluster effects, covariate structures, and informative censoring criteria.Model performance is assessed by evaluating discrimination, prediction error, and calibration metrics.Based on the results, we analyze the performance of each model under different data characteristics and provide guidelines for selecting the most appropriate model in competing risks analysis under given circumstance.