Bertsimas Dimitris, Koulouras Angelos Georgios, Margonis Georgios Antonios
Sloan School of Management and Operations Research Center, E62-560, Massachusetts Institute of Technology, Boston, MA, USA.
Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
NPJ Digit Med. 2024 Nov 3;7(1):307. doi: 10.1038/s41746-024-01291-6.
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.
我们提出了一种新颖的框架，该框架在解决随机临床试验数据亚组分析缺陷的同时，将观察性数据转换为可如同随机数据般使用，从而为精准医学铺平道路。我们的方法通过一个两步过程来应对观察性数据中未观察到的混杂因素的影响，该过程会调整治疗下的预测结果。这些经过调整的预测用于训练决策树，根据患者亚组的特征优化治疗分配，从而实现直观的治疗建议。在包括基因亚队列的胃肠道间质瘤（GIST）数据上实施此框架，结果表明我们的树状建议在外部队列中优于当前指南。此外，我们将此框架的应用扩展到了肢体肉瘤患者的随机对照试验（RCT）数据。尽管最初的试验表明普遍需要治疗，但我们的框架识别出了一部分可能不需要治疗的患者。我们再次在外部队列中成功验证了我们的建议。
