Adaptive treatment and robust control

Clairon, Quentin and Henderson, Robin and Young, N and Wilson, Emma and Taylor, C. James (2021) Adaptive treatment and robust control. Biometrics, 77 (1). pp. 223-236. ISSN 0006-341X

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A control theory perspective on determination of optimal dynamic treatment regimes is considered. The aim is to adapt statistical methodology that has been developed for medical or other biostatistical applications so as to incorporate powerful control techniques that have been designed for engineering or other technological problems. Data tend to be sparse and noisy in the biostatistical area and interest has tended to be in statistical inference for treatment effects. In engineering fields, experimental data can be more easily obtained and reproduced and interest is more often in performance and stability of proposed controllers rather than modelling and inference per se. We propose that modelling and estimation be based on standard statistical techniques but subsequent treatment policy be obtained from robust control. To bring focus, we concentrate on A-learning methodology as developed in the biostatistical literature and h-infinity synthesis from control theory. Simulations and two applications demonstrate robustness of the h-infinity strategy compared to standard A-learning in the presence of model misspecification or measurement error.

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This is the peer reviewed version of the following article: Clairon, Q, Henderson, R, Young, NJ, Wilson, ED, Taylor, CJ. Adaptive treatment and robust control. Biometrics. 2020; 1– 14. which has been published in final form at This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
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?? a-learninganticoagulationcontrolh-infinity synthesismisspecificationpersonalized medicinerobustnessagricultural and biological sciences(all)biochemistry, genetics and molecular biology(all)applied mathematicsstatistics and probabilityimmunology and microb ??
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08 Apr 2020 12:35
Last Modified:
18 Feb 2024 00:57