Doubly Robust Learning for Estimating Individualized Treatment with Censored Data.

TitleDoubly Robust Learning for Estimating Individualized Treatment with Censored Data.
Publication TypeJournal Article
Year of Publication2015
AuthorsZhao, Y Q., D Zeng, E B. Laber, R Song, M Yuan, and M R. Kosorok
Date Published2015 Mar 01

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.

Alternate JournalBiometrika
Original PublicationDoubly robust learning for estimating individualized treatment with censored data.
PubMed ID25937641
PubMed Central IDPMC4414056
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
U01 NS082062 / NS / NINDS NIH HHS / United States