ABSTRACT Precision medicine is currently a topic of great interest in clinical and intervention science. A key component of precision medicine is that it is evidence-based, that is, data-driven, and consequently there has been tremendous interest in estimation of precision medicine strategies using observational or randomized study data. One way to formalize precision medicine is through a treatment regime, which is a sequence of decision rules, one per stage of clinical intervention, that map up-to-date patient information to a recommended treatment. An optimal treatment regime is defined as maximizing the mean of some cumulative clinical outcome if applied to a population of interest. It is well-known that even under simple generative models an optimal treatment regime can be a highly nonlinear function of patient information. Consequently, a focal point of recent methodological research has been the development of flexible models for estimating optimal treatment regimes. However, in many settings, estimation of an optimal treatment regime is an exploratory analysis intended to generate new hypotheses for subsequent research and not to directly dictate treatment to new patients. In such settings, an estimated treatment regime that is interpretable in a domain context may be of greater value than an unintelligible treatment regime built using “black-box” estimation methods. We propose an estimator of an optimal treatment regime composed of a sequence of decision rules, each expressible as a list of “if-then” statements that can be presented as either a paragraph or as a simple flowchart that is immediately interpretable to domain experts. The discreteness of these lists precludes smooth, that is, gradient-based, methods of estimation and leads to nonstandard asymptotics. Nevertheless, we provide a computationally efficient estimation algorithm, prove consistency of the proposed estimator, and derive rates of convergence. We illustrate the proposed methods using a series of simulation examples and application to data from a sequential clinical trial on bipolar disorder. Supplementary materials for this article are available online.

中文翻译：

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使用列表估计最佳治疗方案


摘要 精准医学是目前临床和干预科学界非常感兴趣的话题。精准医疗的一个关键组成部分是它是基于证据的，即数据驱动的，因此人们对使用观察或随机研究数据评估精准医疗策略产生了极大的兴趣。正规化精准医疗的一种方法是通过治疗方案，这是一系列决策规则，临床干预的每个阶段都有一个，将最新的患者信息映射到推荐的治疗。最佳治疗方案被定义为如果应用于感兴趣的人群，则最大化一些累积临床结果的平均值。众所周知，即使在简单的生成模型下，最佳治疗方案也可能是患者信息的高度非线性函数。因此，最近方法学研究的焦点是开发用于估计最佳治疗方案的灵活模型。然而，在许多情况下，最佳治疗方案的估计是一种探索性分析，旨在为后续研究产生新的假设，而不是直接指示新患者的治疗。在这种情况下，在领域上下文中可解释的估计治疗方案可能比使用“黑盒”估计方法构建的难以理解的治疗方案具有更大的价值。我们提出了一种由一系列决策规则组成的最佳治疗方案的估计器，每个决策规则都可以表示为一系列“如果-那么”语句，可以以段落或简单流程图的形式呈现，领域专家可以立即解释。 这些列表的离散性排除了平滑（即基于梯度的）估计方法，并导致非标准渐近。尽管如此，我们提供了一种计算高效的估计算法，证明了所提出的估计器的一致性，并得出了收敛率。我们使用一系列模拟示例和对双相情感障碍序贯临床试验数据的应用来说明所提出的方法。本文的补充材料可在线获取。