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Random survival forest (RSF) is a machine\u2010learning 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 comparing different aspects of performance using measures according to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis) recommendations. Our results show that conclusions solely based on the  index, a performance measure that has been predominantly used in previous studies comparing predictive accuracy of the Cox\u2010PH and RSF model based on real\u2010world observational time\u2010to\u2010event data and that has been criticized by methodologists, may not be generalizable to other aspects of predictive performance. We found that measures of overall performance may generally give more reasonable results, and that the standard log\u2010rank splitting rule used for the RSF may be outperformed by alternative splitting rules, in particular in nonproportional hazards settings. In our simulations, the performance of the RSF suffers less in data with treatment\u2013covariate interactions compared to data where these are absent. The performance of the Cox\u2010PH model is affected by the violation of the proportional hazards\u00a0assumption.<\/jats:p>","DOI":"10.1002\/bimj.70171","type":"journal-article","created":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T05:15:52Z","timestamp":1790399752000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Comparison of the Cox Proportional Hazards Model and Random Survival Forest Algorithm for Predicting Patient\u2010Specific Survival Probabilities in Clinical Trial Data"],"prefix":"10.1002","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0149-479X","authenticated-orcid":false,"given":"Ricarda","family":"Graf","sequence":"first","affiliation":[{"name":"Institute of Mathematics University of Augsburg Augsburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9981-923X","authenticated-orcid":false,"given":"Susan","family":"Todd","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics University of Reading Reading UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3107-8815","authenticated-orcid":false,"given":"M. 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