Quantile-Optimal Treatment Regimes.

TitleQuantile-Optimal Treatment Regimes.
Publication TypeJournal Article
Year of Publication2018
AuthorsWang, Lan, Yu Zhou, Rui Song, and Ben Sherwood
JournalJ Am Stat Assoc
Date Published2018

Finding the optimal treatment regime (or a series of sequential treatment regimes) based on individual characteristics has important applications in areas such as precision medicine, government policies and active labor market interventions. In the current literature, the optimal treatment regime is usually defined as the one that maximizes the average benefit in the potential population. This paper studies a general framework for estimating the quantile-optimal treatment regime, which is of importance in many real-world applications. Given a collection of treatment regimes, we consider robust estimation of the quantile-optimal treatment regime, which does not require the analyst to specify an outcome regression model. We propose an alternative formulation of the estimator as a solution of an optimization problem with an estimated nuisance parameter. This novel representation allows us to investigate the asymptotic theory of the estimated optimal treatment regime using empirical process techniques. We derive theory involving a nonstandard convergence rate and a non-normal limiting distribution. The same nonstandard convergence rate would also occur if the mean optimality criterion is applied, but this has not been studied. Thus, our results fill an important theoretical gap for a general class of policy search methods in the literature. The paper investigates both static and dynamic treatment regimes. In addition, doubly robust estimation and alternative optimality criterion such as that based on Gini's mean difference or weighted quantiles are investigated. Numerical simulations demonstrate the performance of the proposed estimator. A data example from a trial in HIV+ patients is used to illustrate the application.

Alternate JournalJ Am Stat Assoc
Original PublicationQuantile-optimal treatment regimens.
PubMed ID30416233
PubMed Central IDPMC6223317
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States