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Abstract:
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There is a growing focus on making clinical trials more inclusive but how to design trial eligibility is challenging. Here we systematically evaluate the impact 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 advanced non-small cell lung cancer (aNSCLC) trials using data from a nationwide electronic health records (EHR)-derived database of 61,094 patients with aNSCLC. Our analyses revealed that many common criteria, including exclusions based on several laboratory values, had minimal effect on the trial hazard ratios. With a data-driven approach to broaden restrictive criteria, the pool of eligible patients more than doubled across trials, while the overall survival hazard ratio 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 with analyses on other cancers, and with patient safety data from diverse clinical trials. Our data-driven methodology for evaluating eligibility criteria can
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