R&D, Evidence Generation & Decision Sciences, Sanofi, France.
Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, USA.
J Comp Eff Res. 2024 Jul;13(7):e230164. doi: 10.57264/cer-2023-0164. Epub 2024 Jun 13.
Eligibility criteria are pivotal in achieving clinical trial success, enabling targeted patient enrollment while ensuring the trial safety. However, overly restrictive criteria hinder enrollment and study result generalizability. Broadening eligibility criteria enhances the trial inclusivity, diversity and enrollment pace. Liu proposed an AI pathfinder method leveraging real-world data to broaden criteria without compromising efficacy and safety outcomes, demonstrating promise in non-small cell lung cancer trials. To assess the robustness of the methodology, considering diverse qualities of real-world data and to promote its application. We revised the AI pathfinder method, applied it to relapsed and refractory multiple myeloma trials and compared it using two real-world data sources. We modified the assessment and considered a bootstrap confidence interval of the AI pathfinder to enhance the decision robustness. Our findings confirmed the AI pathfinder's potential in identifying certain eligibility criteria, in other words, prior complications and laboratory tests for relaxation or removal. However, a robust quantitative assessment, accounting for trial variability and real-world data quality, is crucial for confident decision-making and prioritizing safety alongside efficacy.
入选标准对于临床试验的成功至关重要，它可以有针对性地招募患者，同时确保试验的安全性。然而，过于严格的入选标准会阻碍招募工作，降低研究结果的普遍性。放宽入选标准可以提高试验的包容性、多样性和招募速度。Liu 提出了一种利用真实世界数据拓宽入选标准的人工智能探索者方法，在不影响疗效和安全性的情况下，在非小细胞肺癌试验中显示出了良好的效果。为了评估该方法的稳健性，我们考虑了真实世界数据的不同质量，并推广了该方法的应用。我们对人工智能探索者方法进行了修订，将其应用于复发性和难治性多发性骨髓瘤试验中，并使用两个真实世界数据源对其进行了比较。我们修改了评估方法，并考虑了人工智能探索者的 Bootstrap 置信区间，以增强决策的稳健性。我们的研究结果证实了人工智能探索者在确定某些入选标准方面的潜力，也就是说，可以放宽先前并发症和实验室检测的要求。然而，稳健的定量评估对于有信心地做出决策和平衡疗效与安全性至关重要，需要考虑到试验的变异性和真实世界数据的质量。
