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∞ -synthesis from control theory. Simulations and two applications demonstrate robustness of the H∞ strategy compared to standard A-learning in the presence of model misspecification or measurement error. This article is protected by copyright. All rights reserved.

中文翻译：

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自适应处理和鲁棒控制

考虑了确定最佳动态治疗方案的控制理论观点。目的是调整为医学或其他生物统计应用而开发的统计方法，以便结合为工程或其他技术问题设计的强大控制技术。生物统计领域的数据往往是稀疏和嘈杂的，而人们的兴趣往往是对治疗效果的统计推断。在工程领域，可以更容易地获得和复制实验数据，并且人们更感兴趣的是所提出的控制器的性能和稳定性，而不是建模和推理本身。我们建议建模和估计基于标准统计技术，但随后的处理策略可以从稳健控制中获得。为了带来焦点，我们专注于生物统计文献中开发的 A-learning 方法和控制理论中的 H∞-综合。模拟和两个应用程序证明了在存在模型错误指定或测量错误的情况下，H∞ 策略与标准 A-learning 相比的鲁棒性。本文受版权保护。版权所有。