Selection of the optimal personalized treatment from multiple treatments with multivariate outcome measures

In this work, we propose a novel method for individualized treatment selection when the treatment response is multivariate. For the <i>K</i> treatment (<i>K ≥</i>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.

本研究针对多变量治疗响应场景，提出一种全新的个性化治疗选择方法。针对K种治疗（K≥2）的场景，研究团队基于采集到的协变量测量结果构建患者特异性评分，并以此为条件，比较各治疗方案基于结局变量构建的适宜指标量。本方法可适配任意数量的治疗方案与结局变量，且可广泛应用于各类模型。所提方法采用排序聚合（rank aggregation）技术，基于治疗性能指标的排序列表估计治疗方案的优先级排序，此类指标包括平滑条件均值，以及某一治疗方案优于其他方案的响应条件概率。该方法具备灵活性，可结合患者与临床医生的偏好，针对个体病例制定最优治疗决策。本研究通过模拟实验验证了所提方法在有限样本下的性能表现。此外，研究团队利用人类免疫缺陷病毒（HIV）与糖尿病（Diabetes）临床试验数据集开展数据分析，以展示所提方法在真实数据中的适用性。