Design and Inovation, 7129Amgen Inc., Thousand Oaks, CA, USA.
Department of Biostatistics, 12284University of Nebraska Medical Center, Omaha, NE, USA.
Stat Methods Med Res. 2023 Feb;32(2):404-424. doi: 10.1177/09622802221144326. Epub 2022 Dec 20.
Assigning optimal treatments to individual patients based on their characteristics is the ultimate goal of precision medicine. Deriving evidence-based recommendations from observational data while considering the causal treatment effects and patient heterogeneity is a challenging task, especially in situations of multiple treatment options. Herein, we propose a reference-free R-learner based on a simplex algorithm for treatment recommendation. We showed through extensive simulation that the proposed method produced accurate recommendations that corresponded to optimal treatment outcomes, regardless of the reference group. We used the method to analyze data from the Systolic Blood Pressure Intervention Trial (SPRINT) and achieved recommendations consistent with the current clinical guidelines.
基于患者特征为个体患者分配最佳治疗方案是精准医学的最终目标。从观察性数据中得出基于证据的推荐意见，同时考虑因果治疗效果和患者异质性，是一项具有挑战性的任务，尤其是在存在多种治疗选择的情况下。在此，我们提出了一种基于单纯形算法的无参考 R-learner 用于治疗推荐。我们通过广泛的模拟表明，无论参考组如何，所提出的方法都能产生准确的推荐，这些推荐与最佳治疗效果相对应。我们使用该方法分析了收缩压干预试验（SPRINT）的数据，并得出了与当前临床指南一致的推荐意见。
