We propose a novel framework that addresses the deficiencies of Randomized clinical trial data subgroup analysis while it transforms ObservAtional Data to be used as if they were randomized, thus paving the road for precision medicine. Our approach counters the effects of unobserved confounding in observational data through a two-step process that adjusts predicted outcomes under treatment. These adjusted predictions train decision trees, optimizing treatment assignments for patient subgroups based on their characteristics, enabling intuitive treatment recommendations. Implementing this framework on gastrointestinal stromal tumors (GIST) data, including genetic sub-cohorts, showed that our tree recommendations outperformed current guidelines in an external cohort. Furthermore, we extended the application of this framework to RCT data from patients with extremity sarcomas. Despite initial trial indications of universal treatment necessity, our framework identified a subset of patients who may not require treatment. Once again, we successfully validated our recommendations in an external cohort.

我们提出了一个新的框架来解决随机临床试验数据亚组分析的缺陷，同时它将观察数据转换为随机数据以供使用，从而为精准医疗铺平道路。我们的方法通过一个两步过程来抵消观察数据中未观察到的混杂的影响，该过程调整治疗下的预测结果。这些调整后的预测可训练决策树，根据患者亚组的特征优化其治疗分配，从而提供直观的治疗建议。在胃肠道间质瘤 （GIST） 数据（包括遗传亚队列）上实施该框架表明，我们的树推荐在外部队列中优于当前指南。此外，我们将该框架的应用扩展到来自肢体肉瘤患者的 RCT 数据。尽管初步试验表明普遍治疗的必要性，但我们的框架确定了可能不需要治疗的患者子集。我们再次在外部同期群中成功验证了我们的建议。