Doubly robust survival trees. (31st March 2016)

• Record Type: Journal Article
• Authors: Steingrimsson, Jon Arni; Diao, Liqun; Molinaro, Annette M.; Strawderman, Robert L.
• Is Part Of: Statistics in medicine. Volume 35:Number 20(2016)
• Page Start: 3595
• Page End: 3612
• Publication Date: 2016-03-31
• Subjects: CART -- censored data -- loss estimation -- inverse probability of censoring weighted estimation -- regression trees -- semiparametric estimation; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6949
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Estimating a patient's mortality risk is important in making treatment decisions. Survival trees are a useful tool and employ recursive partitioning to separate patients into different risk groups. Existing 'loss based' recursive partitioning procedures that would be used in the absence of censoring have previously been extended to the setting of right censored outcomes using inverse probability censoring weighted estimators of loss functions. In this paper, we propose new 'doubly robust' extensions of these loss estimators motivated by semiparametric efficiency theory for missing data that better utilize available data. Simulations and a data analysis demonstrate strong performance of the doubly robust survival trees compared with previously used methods. Copyright © 2016 John Wiley & Sons, Ltd.