ArticleLifetime data analysis2017
L₁ splitting rules in survival forests.
Article in Lifetime data analysis, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Permutation Tests Based on the Copula-Graphic Estimator and Their Use for Survival Tree Construction.Statistics in medicine · 2026Article
- Modelling dependent censoring in time-to-event data using boosting copula regression.Lifetime data analysis · 2025Article
- Random forests for survival data: which methods work best and under what conditions?The international journal of biostatistics · 2024Article
- Comparison of random forest methods for conditional average treatment effect estimation with a continuous treatment.Statistical methods in medical research · 2024Article
- A robust ensemble feature selection approach to prioritize genes associated with survival outcome in high-dimensional gene expression data.Frontiers in systems biology · 2024Article
- Covariance regression with random forests.BMC bioinformatics · 2023Article
- Mixture survival trees for cancer risk classification.Lifetime data analysis · 2022Article
- ROC-guided survival trees and ensembles.Biometrics · 2020Article
- Clinical risk prediction with random forests for survival, longitudinal, and multivariate (RF-SLAM) data analysis.BMC medical research methodology · 2019Observational
- Application of random survival forests in understanding the determinants of under-five child mortality in Uganda in the presence of covariates that satisfy the proportional and non-proportional hazards assumption.BMC research notes · 2017Article
- A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data.BMC medical research methodology · 2017Article
- A Selective Review on Random Survival Forests for High Dimensional Data.Quantitative bio-science · 2017Article
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Authors and funding
3 authors.
Funding
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Abstract
The log-rank test is used as the split function in many commonly used survival trees and forests algorithms. However, the log-rank test may have a significant loss of power in some circumstances, especially when the hazard functions or when the survival functions cross each other in the two compared groups. We investigate the use of the integrated absolute difference between the two children nodes survival functions as the splitting rule. Simulations studies and applications to real data sets show that forests built with this rule produce very good results in general, and that they are often better compared to forests built with the log-rank splitting rule.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.