Quantile-Optimal Treatment Regimes

Lan Wang, Yu Zhou, Rui Song, Ben Sherwood

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


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 article 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 nonnormal 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 article 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. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)1243-1254
Number of pages12
JournalJournal of the American Statistical Association
Issue number523
StatePublished - Jul 3 2018

Bibliographical note

Funding Information:
Dr. Sherwood’s work was done when he was a graduate student at University of Minnesota. Wang’s research is partly supported by NSF DMS-1512267 and DMS-1712706. Song’s research is partly supported by NSF DMS-1555244 and NCI P01 CA142538.

Publisher Copyright:
© 2018, © 2018 American Statistical Association.


  • Dynamic treatment regime
  • Nonstandard asymptotics
  • Optimal treatment regime
  • Precision medicine
  • Quantile criterion


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