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

Journal: Nature
PMID: 33828294

Abstract

There is a growing focus on making clinical trials more inclusive but the design of trial eligibility criteria remains challenging. 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.

Authors

  • Ruishan Liu
    Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
  • Shemra Rizzo
    Genentech, South San Francisco, CA, USA.
  • Samuel Whipple
    Genentech, South San Francisco, CA, USA.
  • Navdeep Pal
    Genentech, South San Francisco, CA, USA.
  • Arturo Lopez Pineda
    Genentech, South San Francisco, CA, USA.
  • Michael Lu
    Genentech, South San Francisco, CA, USA.
  • Brandon Arnieri
    Genentech, South San Francisco, CA, USA.
  • Ying Lu
    Department of Endocrinology, Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China.
  • William Capra
    Genentech, South San Francisco, CA, USA.
  • Ryan Copping
    Genentech, South San Francisco, CA, USA. copping.ryan@gene.com.
  • James Zou
    Department of Biomedical Data Science, Stanford University, Stanford, California.

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