Efficient Log-Rank Updates for Random Survival Forests
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.
Fields of Study & Subjects
Machine Learning and Data Classification
Gaussian Processes and Bayesian Inference
Neural Networks and Applications
Publication details
- Journal / source
- Journal of Computational and Graphical Statistics
- Publisher
- Informa UK Limited
- Pages
- 1–7
- Publication date
- 27 July 2026
- Language
- EN