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Doubly robust survival trees
Author(s) -
Steingrimsson Jon Arni,
Diao Liqun,
Molinaro Annette M.,
Strawderman Robert L.
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6949
Subject(s) - censoring (clinical trials) , estimator , recursive partitioning , inverse probability , computer science , robustness (evolution) , statistics , survival analysis , missing data , econometrics , mathematics , bayesian probability , biochemistry , chemistry , posterior probability , gene
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.