Due to heterogeneity for many chronic diseases, precise personalized medicine, also known as precision medicine, has drawn increasing attentions in the scientific community. One main goal of precision medicine is to develop the most effective tailored therapy for each individual patient. To that end, one needs to incorporate individual characteristics to detect a proper individual treatment rule (ITR), by which suitable decisions on treatment assignments can be made to optimize patients' clinical outcome. For binary treatment settings, outcome weighted learning (OWL) and several of its variations have been proposed recently to estimate the ITR by optimizing the conditional expected outcome given patients' information. However, for multiple treatment scenarios, it remains unclear how to use OWL effectively. It can be shown that some direct extensions of OWL for multiple treatments, such as one-versus-one and one-versus-rest methods, can yield suboptimal performance. In this paper, we propose a new learning method, named Multicategory Outcome weighted Margin-based Learning (MOML), for estimating ITR with multiple treatments. Our proposed method is very general and covers OWL as a special case. We show Fisher consistency for the estimated ITR, and establish convergence rate properties. Variable selection using the sparse l 1 penalty is also considered. Analysis of simulated examples and a type 2 diabetes mellitus observational study are used to demonstrate competitive performance of the proposed method.

中文翻译：

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用于估计个体化治疗规则的多类别结果加权基于边际的学习


由于许多慢性疾病的异质性，精准个性化医疗，也称为精准医疗，越来越受到科学界的关注。精准医学的一个主要目标是为每位患者开发最有效的定制疗法。为此，需要结合个体特征来检测适当的个体治疗规则（ITR），通过该规则可以对治疗分配做出适当的决定，以优化患者的临床结果。对于二元治疗设置，最近提出了结果加权学习 (OWL) 及其几种变体，通过优化给定患者信息的条件预期结果来估计 ITR。然而，对于多种治疗场景，如何有效地使用OWL仍不清楚。可以证明，OWL 对多种治疗的一些直接扩展，例如一对一和一对休息方法，可能会产生次优的性能。在本文中，我们提出了一种新的学习方法，称为多类别结果加权基于利润的学习（MOML），用于估计多种治疗的 ITR。我们提出的方法非常通用，并且涵盖了 OWL 作为一种特殊情况。我们展示了估计 ITR 的 Fisher 一致性，并建立了收敛率属性。还考虑使用稀疏 l 1 惩罚的变量选择。模拟示例分析和 2 型糖尿病观察研究用于证明所提出方法的竞争性能。