Determination of optimal individual treatment rules (ITR) is a rapidly growing area in precision medicine; various parametric and non‐parametric methods have been proposed. Existing methods, however, focus on the mean outcome and thus are sensitive to outliers, skewed and heavy‐tailed outcome distributions. In this paper, we propose an optimal ITR estimation framework using a weighted classifier with robust weights based on measures of similarity. Compared to previous methods in the literature, this two‐stage nonparametric model is novel and enjoys several advantages. First, due to its non‐parametric nature, it is more flexible than regression‐based parametric and semi‐parametric models. Second, the similarity‐based confidence index is essentially a weighted sum of indicator functions depending on the sign of pairwise outcome differences; therefore, it is robust to outliers, skewed and heavy‐tailed outcome distributions. The performance of the proposed approach is demonstrated via simulation studies and an analysis of data from a randomized clinical trial for depression.

中文翻译：

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鲁棒的置信度加权学习指数，用于最佳个性化治疗规则估计

最佳个体治疗规则（ITR）的确定是精密医学领域中快速发展的领域。已经提出了各种参数和非参数方法。但是，现有方法只关注平均结果，因此对异常值，偏斜和重尾结果分布敏感。在本文中，我们提出了一个基于相似性度量的，具有鲁棒权重的加权分类器的最佳ITR估计框架。与文献中的先前方法相比，该两阶段非参数模型是新颖的，并且具有多个优点。首先，由于其非参数性质，它比基于回归的参数和半参数模型更灵活。其次，基于相似度的置信度指数实质上是指标函数的加权和，具体取决于成对结果差异的征兆。因此，它对于异常值，偏斜和重尾的结果分布具有鲁棒性。通过模拟研究和对抑郁症的随机临床试验数据的分析，证明了所提出方法的性能。