title: Doubly robust survival trees.
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.
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, 14853, MD, 21205 U.S.A.
authors.forename: Jon Arni
authors.initials: JA
authors.lastname: Steingrimsson
authors.affiliation: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, N2L 3G1, ON, CANADA.
authors.forename: Liqun
authors.initials: L
authors.lastname: Diao
authors.affiliation: Department of Neurological Surgery, University of California, San Francisco, 94143-0372, CA, U.S.A.
authors.forename: Annette M
authors.initials: AM
authors.lastname: Molinaro
authors.affiliation: Department of Biostatistics and Computational Biology, University of Rochester, Rochester, 14642, NY, U.S.A.
authors.forename: Robert L
authors.initials: RL
authors.lastname: Strawderman
mesh_terms: D000068598:Data Accuracy; D006801:Humans; D015233:Models, Statistical; D009026:Mortality; D012307:Risk Factors; D016019:Survival Analysis
keywords: CART; censored data; inverse probability of censoring weighted estimation; loss estimation; regression trees; semiparametric estimation