Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, Kentucky, USA.
Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, Hawaii, USA.
Stat Med. 2022 Jul 10;41(15):2695-2710. doi: 10.1002/sim.9377. Epub 2022 Mar 16.
In this work, we propose a method for individualized treatment selection when there are correlated multiple responses for the treatment ( ) scenario. Here we use ranks of quantiles of 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 using any number of quantiles and it can be applied for a broad set of models. We propose a rank aggregation technique for combining several lists of ranks where both these lists and elements within each list can be correlated. The method has the flexibility to incorporate patient and clinician preferences into 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 illustrations using two different datasets from diabetes and HIV-1 clinical trials to show the applicability of the proposed procedure for real data.
在这项工作中，我们提出了一种方法，用于当存在相关的多个响应时进行个体化治疗选择（ ）情况。在这里，我们使用基于从收集的协变量测量中构建的患者特定分数的每个治疗条件下的结果变量的分位数的等级。我们的方法使用任意数量的分位数覆盖任意数量的治疗和结果变量，并且可以应用于广泛的模型。我们提出了一种用于组合多个等级列表的等级聚合技术，其中这两个列表和每个列表中的元素都可以相关。该方法具有灵活性，可以根据患者的个人情况将患者和临床医生的偏好纳入最佳治疗决策中。一项模拟研究证明了该方法在有限样本中的性能。我们还使用来自糖尿病和 HIV-1 临床试验的两个不同数据集进行说明，以展示所提出的程序在实际数据中的适用性。
