Survival forests under test: Impact of the proportional hazards assumption on prognostic and predictive forests for amyotrophic lateral sclerosis survival. (May 2020)

• Record Type: Journal Article
• Authors: Korepanova, Natalia; Seibold, Heidi; Steffen, Verena; Hothorn, Torsten
• Is Part Of: Statistical methods in medical research. Volume 29:Number 5(2020)
• Page Start: 1403
• Page End: 1419
• Publication Date: 2020-05
• Subjects: Random forest -- transformation model -- conditional survivor function -- conditional hazard function -- survival trees -- amyotrophic lateral sclerosis; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280219862586
• Languages: English
• ISSNs: 0962-2802

Abstract

We investigate the effect of the proportional hazards assumption on prognostic and predictive models of the survival time of patients suffering from amyotrophic lateral sclerosis. We theoretically compare the underlying model formulations of several variants of survival forests and implementations thereof, including random forests for survival, conditional inference forests, Ranger, and survival forests with L 1 splitting, with two novel variants, namely distributional and transformation survival forests. Theoretical considerations explain the low power of log-rank-based splitting in detecting patterns in non-proportional hazards situations in survival trees and corresponding forests. This limitation can potentially be overcome by the alternative split procedures suggested herein. We empirically investigated this effect using simulation experiments and a re-analysis of the Pooled Resource Open-Access ALS Clinical Trials database of amyotrophic lateral sclerosis survival, giving special emphasis to both prognostic and predictive models.