In this work, we propose a novel method for individualized treatment selection when the treatment response is multivariate. For the K treatment (K ≥2) scenario we compare quantities that are suitable indexes based on outcome variables for each treatment conditional on patient-specific scores constructed from collected covariate measurements. Our method covers any number of treatments and outcome variables, and it can be applied for a broad set of models. The proposed method uses a rank aggregation technique to estimate an ordering of treatments based on ranked lists of treatment performance measures such as smooth conditional means and conditional probability of a response for one treatment dominating others. The method has the flexibility to incorporate patient and clinician preferences to the optimal treatment decision on an individual case basis. A simulation study demonstrates the performance of the proposed method in finite samples. We also present data analyses using HIV and Diabetes clinical trials data to show the applicability of the proposed procedure for real data.

中文翻译：

---

从具有多种结果指标的多种治疗中选择最佳的个性化治疗。

在这项工作中，我们提出了一种新颖的方法，用于当治疗反应为多变量时进行个体化治疗选择。对于K治疗（K≥2）方案，我们根据每种变量的结局变量，比较适合的指标量，这些变量的条件是根据从收集的协变量测量结果中得出的患者特定评分来确定。我们的方法涵盖了任何数量的治疗方法和结果变量，并且可以应用于多种模型。所提出的方法使用等级汇总技术，基于治疗效果测度的排序列表（例如，光滑的条件均值和对一种治疗为主的其他治疗的响应的条件概率）来估计治疗的顺序。该方法具有灵活性，可以根据具体情况将患者和临床医生的喜好纳入最佳治疗决策。仿真研究证明了该方法在有限样本中的性能。我们还提供了使用HIV和糖尿病临床试验数据进行的数据分析，以显示所提出的程序对真实数据的适用性。