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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.

SUBMITTER: Steingrimsson JA

PROVIDER: S-EPMC7286558 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Doubly robust survival trees.

Steingrimsson Jon Arni JA Diao Liqun L Molinaro Annette M AM Strawderman Robert L RL

Statistics in medicine 20160331 20

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' extens ...[more]

PMID: 27037609

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