In this work, we propose a novel method for individualized treatment selection when the treatment response is multivariate. Our method covers any number of treatments and it can be applied for a broad set of models. The proposed method uses a Mahalanobis-type distance measure to establish an ordering of treatments based on treatment performance measures. Our investigation in this work deals with means of responses conditional on lower dimensional composite scores based on covariates where these scores are built using single index models to approximate mean responses against patient covariates. Smoothed estimates of such conditional means are combined to construct an estimate of the aforementioned distance measure, which is then used to estimate the optimal treatment. An empirical study demonstrates the performance of the proposed method in finite samples. We also present a data analysis using an HIV clinical trial data to show the applicability of the proposed procedure for real data.

中文翻译：

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具有多变量结果的个性化治疗计划

在这项工作中，我们提出了一种当治疗反应是多变量时进行个体化治疗选择的新方法。我们的方法涵盖任意数量的治疗方法，并且可以应用于广泛的模型。所提出的方法使用马哈拉诺比斯型距离测量来根据治疗性能测量建立治疗顺序。我们在这项工作中的研究涉及以基于协变量的低维综合分数为条件的反应方法，其中这些分数是使用单指数模型构建的，以近似针对患者协变量的平均反应。组合此类条件均值的平滑估计以构建上述距离测量的估计，然后使用该估计来估计最佳治疗。实证研究证明了所提出的方法在有限样本中的性能。我们还使用 HIV 临床试验数据进行数据分析，以显示所提出的程序对真实数据的适用性。