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Doubly Robust Estimation of Optimal Dosing Strategies

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  • Juliana Schulz
  • Erica E. M. Moodie

Abstract

The goal of precision medicine is to tailor treatment strategies on an individual patient level. Although several estimation techniques have been developed for determining optimal treatment rules, the majority of methods focus on the case of a dichotomous treatment, an example being the dynamic weighted ordinary least squares regression approach of Wallace and Moodie. We propose an extension to the aforementioned framework to allow for a continuous treatment with the ultimate goal of estimating optimal dosing strategies. The proposed method is shown to be doubly robust against model misspecification whenever the implemented weights satisfy a particular balancing condition. A broad class of weight functions can be derived from the balancing condition, providing a flexible regression based estimation method in the context of adaptive treatment strategies for continuous valued treatments. Supplementary materials for this article are available online.

Suggested Citation

  • Juliana Schulz & Erica E. M. Moodie, 2021. "Doubly Robust Estimation of Optimal Dosing Strategies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(533), pages 256-268, March.
    • Handle: RePEc:taf:jnlasa:v:116:y:2021:i:533:p:256-268
    DOI: 10.1080/01621459.2020.1753521
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    Cited by:

    1. Chunrong Ai & Yue Fang & Haitian Xie, 2024. "Data-driven Policy Learning for a Continuous Treatment," Papers 2402.02535, arXiv.org.
    2. Erica E. M. Moodie & Janie Coulombe & Coraline Danieli & Christel Renoux & Susan M. Shortreed, 2022. "Privacy-preserving estimation of an optimal individualized treatment rule: a case study in maximizing time to severe depression-related outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(3), pages 512-542, July.

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