Evaluating eligibility criteria of oncology trials using real-world data and AI

Ruishan Liu, Shemra Rizzo, Samuel Whipple, Navdeep Pal, Arturo Lopez Pineda, Michael Lu, Brandon Arnieri, Ying Lu, William Capra, Ryan Copping () and James Zou ()
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Ruishan Liu: Stanford University
Shemra Rizzo: Genentech
Samuel Whipple: Genentech
Navdeep Pal: Genentech
Arturo Lopez Pineda: Genentech
Michael Lu: Genentech
Brandon Arnieri: Genentech
Ying Lu: Stanford University
William Capra: Genentech
Ryan Copping: Genentech
James Zou: Stanford University

Nature, 2021, vol. 592, issue 7855, 629-633

Abstract: Abstract There is a growing focus on making clinical trials more inclusive but the design of trial eligibility criteria remains challenging1–3. 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.

Date: 2021
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DOI: 10.1038/s41586-021-03430-5

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