Comparison of the Cox proportional hazards model and Random Survival Forest algorithm for predicting patient-specific survival probabilities in clinical trial data

• URL: http://arxiv.org/abs/2502.03119v1
• Date: Wed, 05 Feb 2025 12:26:43 GMT
• Title: Comparison of the Cox proportional hazards model and Random Survival Forest algorithm for predicting patient-specific survival probabilities in clinical trial data
• Authors: Ricarda Graf, Susan Todd, M. Fazil Baksh,
• Abstract summary: Cox proportional hazards model is often used for model development in randomized controlled trials (RCT) with time-to-event outcomes. Random survival forests (RSF) is a machine-learning algorithm known for its high predictive performance. We conduct a comprehensive neutral comparison study to compare the predictive performance of Cox regression and RSF in real-world as well as simulated data.
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• Abstract: The Cox proportional hazards model is often used for model development in data from randomized controlled trials (RCT) with time-to-event outcomes. Random survival forests (RSF) is a machine-learning algorithm known for its high predictive performance. We conduct a comprehensive neutral comparison study to compare the predictive performance of Cox regression and RSF in real-world as well as simulated data. Performance is compared using multiple performance measures according to recommendations for the comparison of prognostic prediction models. We found that while the RSF usually outperforms the Cox model when using the $C$ index, Cox model predictions may be better calibrated. With respect to overall performance, the Cox model often exceeds the RSF in nonproportional hazards settings, while otherwise the RSF typically performs better especially for smaller sample sizes. Overall performance of the RSF is more affected by higher censoring rates, while overall performance of the Cox model suffers more from smaller sample sizes.

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