Q- and A-learning methods for estimating optimal dynamic treatment regimes

• Autores: Phillip J. Schulte, Anastasios A. Tsiatis, Eric B. Laber, Marie Davidian
• Localización: Statistical science, ISSN 0883-4237, Vol. 29, Nº. 4, 2014 (Ejemplar dedicado a: Semiparametrics and Causal Inference), págs. 640-661
• Idioma: inglés
• DOI: 10.1214/13-sts450
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• Resumen

  • In clinical practice, physicians make a series of treatment decisions over the course of a patient�s disease based on his/her baseline and evolving characteristics. A dynamic treatment regime is a set of sequential decision rules that operationalizes this process. Each rule corresponds to a decision point and dictates the next treatment action based on the accrued information.

    Using existing data, a key goal is estimating the optimal regime, that, if followed by the patient population, would yield the most favorable outcome on average. Q- and A-learning are two main approaches for this purpose. We provide a detailed account of these methods, study their performance, and illustrate them using data from a depression study.