A treatment regime maps observed patient characteristics to a recommended treatment. Recent technological advances have increased the quality, accessibility, and volume of patient‐level data; consequently, there is a growing need for powerful and flexible estimators of an optimal treatment regime that can be used with either observational or randomized clinical trial data. We propose a novel and general framework that transforms the problem of estimating an optimal treatment regime into a classification problem wherein the optimal classifier corresponds to the optimal treatment regime. We show that commonly employed parametric and semi‐parametric regression estimators, as well as recently proposed robust estimators of an optimal treatment regime can be represented as special cases within our framework. Furthermore, our approach allows any classification procedure that can accommodate case weights to be used without modification to estimate an optimal treatment regime. This introduces a wealth of new and powerful learning algorithms for use in estimating treatment regimes. We illustrate our approach using data from a breast cancer clinical trial. Copyright © 2012 John Wiley & Sons, Ltd.

中文翻译：

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从分类角度估计最佳治疗方案。

治疗方案将观察到的患者特征映射到推荐的治疗。最近的技术进步提高了患者级数据的质量、可访问性和数量；因此，对可与观察性或随机临床试验数据一起使用的最佳治疗方案的强大而灵活的估计器的需求不断增长。我们提出了一个新颖的通用框架，将估计最佳治疗方案的问题转化为分类问题，其中最佳分类器对应于最佳治疗方案。我们表明，常用的参数和半参数回归估计量，以及最近提出的最佳治疗方案的稳健估计量可以表示为我们框架内的特殊情况。此外，我们的方法允许使用任何可以适应病例权重的分类程序，无需修改即可估计最佳治疗方案。这引入了大量新的强大的学习算法，用于估计治疗方案。我们使用来自乳腺癌临床试验的数据来说明我们的方法。版权所有 © 2012 John Wiley & Sons, Ltd.