![]() The Flatiron Health database that we use has effectively been used to analyse outcomes of patients with lung cancer after immunotherapies 25, 26. Our study differs from these studies in that we focus on evaluating the effect of relaxing specific eligibility criteria on treatment efficacy and cohort size in a real-world population. Recent research also used EHR data to evaluate how different eligibility criteria can affect the number of adverse events associated with COVID-19 that are observed in the selected cohort 24. Several studies have introduced approaches to quantify the difference between the study samples of a clinical trial and the target population that can use the treatment 22, 23. Artificial intelligence can screen patients that meet eligibility 14, 15, 16, predict which patients are more likely to enrol in trials 17, 18 and extract features from electronic health records (EHRs) 19, 20, 21. In an evaluation by the American Society of Clinical Oncology, 56% of surveyed clinicians agreed that some criteria are too stringent and harm the trial, but no agreement could be reached on the removal of specific criteria, given the available data 9.ĭata-driven algorithms combined with real-world data can potentially improve several aspects of clinical trials 11, 12, 13. Some eligibility criteria are included to reduce the risks of severe toxicity adverse events, which is a critical consideration 10. Even trials with similar mechanisms that target the same disease often use different eligibility criteria, possibly owing to legacy protocols. However, how to broaden eligibility remains a major challenge. There is therefore a great need to have faster trial accrual and better generalizability, with data-driven eligibility criteria 7, 8, 9, 10. Restrictive trials do not fully capture the efficacy and safety of the drug in the populations that will use the drug after approval 1. The US Food and Drug Administration has also emphasized that certain populations are usually excluded from clinical trials without solid clinical justification. The US National Cancer Institute concluded that eligibility criteria arbitrarily eliminate patients and should be simplified and broadened 5, 6. As a result, 86% of clinical trials failed to complete their recruitment within the targeted time 4. For example, around 80% of patients with advanced non-small-cell lung cancer (aNSCLC) did not meet the criteria of the analysed trials 3. Overly restrictive, and sometimes poorly justified 1, eligibility criteria are a key barrier that leads to low enrolment in clinical trials 2. Our data-driven methodology for evaluating eligibility criteria can facilitate the design of more-inclusive trials while maintaining safeguards for patient safety. We further support our findings through analyses of other types of cancer and patient-safety data from diverse clinical trials. This suggests that many patients who were not eligible under the original trial criteria could potentially benefit from the treatments. 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. Our analyses reveal that many common criteria, including exclusions based on several laboratory values, had a minimal effect on the trial hazard ratios. 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. 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. There is a growing focus on making clinical trials more inclusive but the design of trial eligibility criteria remains challenging 1, 2, 3.
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