Efficient Log-Rank Updates for Random Survival Forests

Erik Sverdrup; James Yang; Michael LeBlanc

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arXiv · 2510.03665

Efficient Log-Rank Updates for Random Survival Forests

Erik Sverdrup·James Yang·Michael LeBlanc

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Abstract

Random survival forests are widely used for estimating covariate-conditional survival functions under right-censoring. Their standard log-rank splitting criterion is typically recomputed at each candidate split. This O(M) cost per split, with M the number of distinct event times in a node, creates a bottleneck for large cohort datasets with long follow-up. We revisit approximations proposed by LeBlanc and Crowley (1995) and develop simple constant-time updates for the log-rank criterion. The method is implemented in grf for R and reduces training time on large datasets while preserving predictive accuracy.

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Erik Sverdrup, James Yang, Michael LeBlanc. 2025-10-04. Efficient Log-Rank Updates for Random Survival Forests. https://arxiv.org/abs/2510.03665

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