Doubly robust learning for estimating individualized treatment with censored data

Individualized treatment rules recommend treatments based on individual patient characteristics in order to maximize clinical benefit. When the clinical outcome of interest is survival time, estimation is often complicated by censoring. We develop nonparametric methods for estimating an optimal indi...

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Bibliographic Details
Published in:Biometrika 2015-03, Vol.102 (1), p.151-168
Main Authors: ZHAO, Y. Q., ZENG, D., LABER, E. B., SONG, R., YUAN, M., KOSOROK, M. R.
Format: Article
Language:English
Subjects:
Clinical trials
Estimating techniques
Mathematical models
Medical treatment
Parameter estimation
Studies
Survival analysis
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Summary:Individualized treatment rules recommend treatments based on individual patient characteristics in order to maximize clinical benefit. When the clinical outcome of interest is survival time, estimation is often complicated by censoring. We develop nonparametric methods for estimating an optimal individualized treatment rule in the presence of censored data. To adjust for censoring, we propose a doubly robust estimator which requires correct specification of either the censoring model or survival model, but not both; the method is shown to be Fisher consistent when either model is correct. Furthermore, we establish the convergence rate of the expected survival under the estimated optimal individualized treatment rule to the expected survival under the optimal individualized treatment rule. We illustrate the proposed methods using simulation study and data from a Phase III clinical trial on non-small cell lung cancer.
ISSN:0006-3444
1464-3510
DOI:10.1093/biomet/asu050