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Systematic Evaluation of the Effect of Broadening Eligibility Criteria for Oncology Trials Using Real-World Data (310007)

Brandon Arnieri, Genentech 
William Capra, Genentech 
Ryan Copping, Genentech 
Ruishan Liu, Stanford 
Arturo Lopez Pineda, Genentech 
Michael Lu, Genentech 
Ying Lu, Stanford 
Navdeep Pal, Genentech 
*Shemra Rizzo, Genentech 
Samuel Whipple, Genentech 
Jame Zou, Stanford 

Keywords: eligibility criteria, oncology, clinical trials, real-world data, data science

Trials with overly restrictive eligibility criteria, that sometimes lack clinical justification, do not fully capture the efficacy and safety of the drug in the populations that will use it after approval. However, how to broaden eligibility remains a major challenge. In this study, we systematically evaluated the effect of different eligibility criteria on the hazard ratio (HR) of completed trials of advanced non-small cell lung cancer using electronic health records from over 60K patients in the US. Our analyses revealed that many common criteria, including lab-based exclusions, had minimal effect on the HRs. When using a data-driven approach to broaden restrictive criteria, the pool of eligible patients more than doubled, including more women, while the HR decreased by 0.05 on average. 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 various clinical trials.