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Doubly robust learning for estimating individualized treatment with censored data. (English) Zbl 1345.62092

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.

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)
62N01 Censored data models
62J07 Ridge regression; shrinkage estimators (Lasso)
62P10 Applications of statistics to biology and medical sciences; meta analysis
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