Estimation of optimal dynamic treatment regimes.

TitleEstimation of optimal dynamic treatment regimes.
Publication TypeJournal Article
Year of Publication2014
AuthorsZhao, Ying-Qi, and Eric B. Laber
JournalClin Trials
Date Published2014 Aug

BACKGROUND: Recent advances in medical research suggest that the optimal treatment rules should be adaptive to patients over time. This has led to an increasing interest in studying dynamic treatment regime, a sequence of individualized treatment rules, one per stage of clinical intervention, which maps present patient information to a recommended treatment. There has been a recent surge of statistical work for estimating optimal dynamic treatment regimes from randomized and observational studies. The purpose of this article is to review recent methodological progress and applied issues associated with estimating optimal dynamic treatment regimes.METHODS: We discuss sequential multiple assignment randomized trials, a clinical trial design used to study treatment sequences. We use a common estimator of an optimal dynamic treatment regime that applies to sequential multiple assignment randomized trials data as a platform to discuss several practical and methodological issues.RESULTS: We provide a limited survey of practical issues associated with modeling sequential multiple assignment randomized trials data. We review some existing estimators of optimal dynamic treatment regimes and discuss practical issues associated with these methods including model building, missing data, statistical inference, and choosing an outcome when only non-responders are re-randomized. We mainly focus on the estimation and inference of dynamic treatment regimes using sequential multiple assignment randomized trials data. Dynamic treatment regimes can also be constructed from observational data, which may be easier to obtain in practice; however, care must be taken to account for potential confounding.

Alternate JournalClin Trials
Original PublicationEstimation of optimal dynamic treatment regimes.
PubMed ID24872361
PubMed Central IDPMC4247353
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R13 CA132565 / CA / NCI NIH HHS / United States