title: L₁ splitting rules in survival forests.
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.
authors.affiliation: Department of Decision Sciences, HEC Montréal, 3000 chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 2A7, Canada.
authors.forename: Hoora
authors.initials: H
authors.lastname: Moradian
authors.affiliation: Department of Decision Sciences, HEC Montréal, 3000 chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 2A7, Canada. denis.larocque@hec.ca.
authors.forename: Denis
authors.initials: D
authors.lastname: Larocque
authors.affiliation: Department of Decision Sciences, HEC Montréal, 3000 chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 2A7, Canada.
authors.forename: François
authors.initials: F
authors.lastname: Bellavance
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016208:Databases, Factual / Q000706:statistics & numerical data; D006801:Humans; D053208:Kaplan-Meier Estimate; D015993:Life Tables; D015233:Models, Statistical; D016016:Proportional Hazards Models; D016019:Survival Analysis
keywords: Ensemble methods; Random forests; Right-censored data; Survival data; Survival forests