Comparison of oblique random survival forest, random survival forest, and statistical models for time-to-event data using simulation study.

ABSTRACT: Time-to-event (TTE) machine learning (ML) algorithms are increasingly utilized in prognostic models, but systematic evaluation is lacking to identify their strengths and limitations. We compared TTE ML algorithms - Oblique Random Survival Forest (ORSF) and Random Survival Forest (RSF) to statistical models (SMs) - Cox Proportional Hazards (Cox PH) and Penalized Cox PH, examining their predictive performance and computational time. Eighteen scenarios were generated with varying censoring rates, sample sizes, and predictor effects, assuming the PH assumption. Performance was evaluated using Harrell's C-index and IBS, with differences assessed using One-Way Repeated Measures ANOVA. In the linear with additive effects scenario, SMs outperformed RSF in terms of C-indices and IBS scores, with ne

SUBMITTER: Suliman A 

PROVIDER: S-EPMC12712062 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature