ArticleThe international journal of biostatistics2024
Random forests for survival data: which methods work best and under what conditions?
Article in The international journal of biostatistics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multi-omics analysis of arginine metabolism in ovarian cancer: A prognostic signature and GTF2F2-driven stromal remodeling.Translational oncology · 2026Article
- Age-specific impact of different immunosuppressive regimens on kidney transplant outcomes: clinical evidence from the scientific registry of transplant recipients.Frontiers in nephrology · 2026Article
- Sex-Stratified Prediction Models for 5-Year Nonalcoholic Fatty Liver Disease Risk in Thyroid Cancer Patients: A Nationwide Cohort Study.Biomedicines · 2025Article
- Machine learning predicts prognosis in patients with gastroenteropancreatic neuroendocrine tumors with liver metastases.Discover oncology · 2025Article
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Authors and funding
3 authors.
Funding
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Abstract
Few systematic comparisons of methods for constructing survival trees and forests exist in the literature. Importantly, when the goal is to predict a survival time or estimate a survival function, the optimal choice of method is unclear. We use an extensive simulation study to systematically investigate various factors that influence survival forest performance - forest construction method, censoring, sample size, distribution of the response, structure of the linear predictor, and presence of correlated or noisy covariates. In particular, we study 11 methods that have recently been proposed in the literature and identify 6 top performers. We find that all the factors that we investigate have significant impact on the methods' relative accuracy of point predictions of survival times and survival function estimates. We use our results to make recommendations for which methods to use in a given context and offer explanations for the observed differences in relative performance.
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Registered trials
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