There is a growing focus on making clinical trials more inclusive but the design of trial eligibility criteria remains challenging^1,2,3. Here we systematically evaluate the effect of different eligibility criteria on cancer trial populations and outcomes with real-world data using the computational framework of Trial Pathfinder. We apply Trial Pathfinder to emulate completed trials of advanced non-small-cell lung cancer using data from a nationwide database of electronic health records comprising 61,094 patients with advanced non-small-cell lung cancer. Our analyses reveal that many common criteria, including exclusions based on several laboratory values, had a minimal effect on the trial hazard ratios. When we used a data-driven approach to broaden restrictive criteria, the pool of eligible patients more than doubled on average and the hazard ratio of the overall survival decreased by an average of 0.05. This suggests that many patients who were not eligible under the original trial criteria could potentially benefit from the treatments. We further support our findings through analyses of other types of cancer and patient-safety data from diverse clinical trials. Our data-driven methodology for evaluating eligibility criteria can facilitate the design of more-inclusive trials while maintaining safeguards for patient safety.

人们越来越关注使临床试验更具包容性，但试验资格标准的设计仍然具有挑战性^1,2,3. 在这里，我们使用 Trial Pathfinder 的计算框架，利用真实世界的数据系统地评估不同资格标准对癌症试验人群和结果的影响。我们应用 Trial Pathfinder 来模拟已完成的晚期非小细胞肺癌试验，使用来自全国电子健康记录数据库的数据，该数据库包含 61,094 名晚期非小细胞肺癌患者。我们的分析表明，许多常见标准，包括基于多个实验室值的排除，对试验风险比的影响很小。当我们使用数据驱动的方法来扩大限制性标准时，符合条件的患者人数平均增加了一倍以上，总生存期的风险比平均下降了 0.05。这表明许多不符合原始试验标准的患者可能会从治疗中受益。我们通过分析其他类型的癌症和来自不同临床试验的患者安全数据进一步支持我们的发现。我们用于评估资格标准的数据驱动方法可以促进更具包容性的试验的设计，同时保持对患者安全的保障。