Work

Efficient Log-Rank Updates for Random Survival Forests

Year: 2026

Type: article

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.

Source: Journal of Computational and Graphical Statistics

DOI: 10.1080/10618600.2026.2709001

Authors: Erik Sverdrup, James Yang, Michael LeBlanc

Institutions: Monash University, Stanford University, University of Washington, Fred Hutch Cancer Center

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4414969671

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