A dynamic treatment regime consists of a sequence of decision rules, one per stage of intervention, that dictate how to individualize treatments to patients based on evolving treatment and covariate history. These regimes are particularly useful for managing chronic disorders, and fit well into the larger paradigm of personalized medicine. They provide one way to operationalize a clinical decision support system. Statistics plays a key role in the construction of evidence-based dynamic treatment regimes - informing best study design as well as efficient estimation and valid inference. Due to the many novel methodological challenges it offers, this area has been growing in popularity among statisticians in recent years. In this article, we review the key developments in this exciting field of research. In particular, we discuss the sequential multiple assignment randomized trial designs, estimation techniques like Q-learning and marginal structural models, and several inference techniques designed to address the associated non-standard asymptotics. We reference software, whenever available. We also outline some important future directions.

中文翻译：

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动态治疗制度。

动态治疗方案由一系列决策规则组成，每个干预阶段一个决策规则，用于指示如何根据不断发展的治疗方法和协变历史对患者进行个体化治疗。这些方案对于管理慢性疾病特别有用，并且非常适合个性化医学的更大范式。它们提供了一种操作临床决策支持系统的方法。统计数据在建立基于证据的动态治疗方案中起着关键作用-告知最佳研究设计以及有效的估计和有效的推论。由于它提供了许多新颖的方法论挑战，因此近年来该领域在统计学家中的流行度越来越高。在本文中，我们回顾了这一激动人心的研究领域中的关键发展。特别是，我们讨论了顺序多次分配随机试验设计，诸如Q学习和边际结构模型之类的估算技术，以及旨在解决相关非标准渐近现象的几种推理技术。只要有可用，我们都会引用软件。我们还概述了一些重要的未来方向。