Doubly robust survival trees

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 cens...

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Bibliographic Details
Published in:Statistics in medicine 2016-09, Vol.35 (20), p.3595-3612
Main Authors: Steingrimsson, Jon Arni, Diao, Liqun, Molinaro, Annette M., Strawderman, Robert L.
Format: Article
Language:English
Subjects:
CART
censored data
Data Accuracy
Data analysis
Estimating techniques
Humans
inverse probability of censoring weighted estimation
loss estimation
Medical statistics
Medical treatment
Models, Statistical
Mortality
Probability
regression trees
Risk Factors
semiparametric estimation
Simulation
Survival Analysis
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Summary: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.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.6949