aceComparison of the Cox Proportional Hazards Model and Random Survival Forest Algorithm for Predicting Patient-Specific Survival Probabilities in Clinical Trial Data
The Cox proportional hazards model is often used to analyze data from randomized controlled trials (RCTs) with time-to-event outcomes. Random survival forest (RSF) is a machine-learning algorithm known for its high predictive performance. We conduct a comprehensive neutral comparison study to compare the performance of Cox regression and RSF in various simulation scenarios based on two reference datasets from RCTs. The motivation is to identify settings in which one method is preferable over the other when compa…
In simulated RCT time-to-event data, random survival forest retained predictive performance better when treatment-covariate interactions were present than when they were absent.
In the same RCT simulations, the standard log-rank splitting rule for random survival forest was outperformed by alternative rules in nonproportional hazards settings, limiting its predictive advantage.
Findings are based on simulation scenarios derived from only two RCT reference datasets, and conclusions based solely on C index may not generalize to other performance aspects.
Evidence
- Peer-reviewedBiometrical Journal2026-10-01
How should this claim be treated?
Truvace Impact Record TRV-2026-1232, v1: “Comparison of the Cox Proportional Hazards Model and Random Survival Forest Algorithm for Predicting Patient-Specific Survival Probabilities in Clinical Trial Data.” Truvace, 2026-10-01. /record/TRV-2026-1232 (accessed at citation time). sha256 f5df053353ee0e84…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1232 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.