Estimating Optimal Treatment Regimes from a Classification Perspective.
Estimating Optimal Treatment Regimes from a Classification Perspective.
复制标题
作者：
Zhang, Baqun;Tsiatis, Anastasios A;Davidian, Marie;Zhang, Min;Laber, Eric
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水平数据的质量，可访问性和数量;因此，越来越需要强大而灵活的最佳治疗方案估计器，可用于观察性或随机临床试验数据。我们提出了一种新的和一般的框架，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，以及最近提出的鲁棒估计的最佳治疗方案可以表示为特殊情况下，在我们的框架。此外，我们的方法允许任何分类程序，可以容纳的情况下的权重被用来估计一个最佳的治疗方案，而无需修改。这引入了大量新的和强大的学习算法，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。
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.