An individualized treatment rule is often employed to maximize a certain patient‐specific clinical outcome based on his/her clinical or genomic characteristics as well as heterogeneous response to treatments. Although developing such a rule is conceptually important to personalized medicine, existing methods such as the partial least squares suffers from the difficulty of indirect maximization of a patient's clinical outcome, while the outcome weighted learning is not robust against any perturbation of the outcome. In this article, we propose a weighted ψ‐learning method to optimize an individualized treatment rule, which is robust against any data perturbation near the decision boundary by seeking the maximum separation. To solve non‐convex minimization, we employ a difference convex algorithm to relax the non‐convex minimization iteratively based on a decomposition of the cost function into a difference of two convex functions. On this ground, we also introduce a variable selection method for further removing redundant variables for a higher performance. Finally, we illustrate the proposed method by simulations and a lung health study, and we demonstrate that it yields higher performances in terms of accuracy of prediction of individualized treatment.

中文翻译：

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个性化治疗规则的结果加权 ψ-学习


通常根据患者的临床或基因组特征以及对治疗的异质反应，采用个体化治疗规则来最大化特定患者的临床结果。尽管制定这样的规则对于个性化医疗在概念上很重要，但现有方法（例如偏最小二乘法）难以间接最大化患者的临床结果，而结果加权学习对于结果的任何扰动并不稳健。在本文中，我们提出了一种加权ψ学习方法来优化个性化治疗规则，该方法通过寻求最大分离，对决策边界附近的任何数据扰动具有鲁棒性。为了解决非凸最小化问题，我们采用差分凸算法，基于将成本函数分解为两个凸函数的差来迭代地放松非凸最小化。基于此，我们还引入了一种变量选择方法，以进一步去除冗余变量以获得更高的性能。最后，我们通过模拟和肺部健康研究说明了所提出的方法，并证明它在个体化治疗预测的准确性方面具有更高的性能。