RANDOM SURVIVAL FORESTS

• 成果类型: Article
• 署名作者: Ishwaran, Hemant; Kogalur, Udaya B.; Blackstone, Eugene H.; Lauer, Michael S.
• 署名单位: Cleveland Clinic Foundation; Columbia University; Cleveland Clinic Foundation; National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI)
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/08-AOAS169
• 发表日期: 2008
• 页码: 841-860
• 关键词: regression trees; overweight; obesity; Underweight

摘要

We introduce random survival forests, a random forests method for the analysis of right-censored survival data. New survival splitting rules for growing survival trees are introduced, as is a new missing data algorithm for imputing missing data. A conservation-of-events principle for survival forests is introduced and used to define ensemble mortality, a simple interpretable measure of mortality that can be used as a predicted outcome. Several illustrative examples are given, including a case study of the prognostic implications of body mass for individuals with coronary artery disease. Computations for all examples were implemented using the freely available R-software package. randomSurvivalForest.