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Using real-word data to evaluate the effects of broadening eligibility criteria in oncology trials

Enrique Sanz-Garcia1, Benjamin Haibe-Kains2, Lillian L Siu1

  • 1Princess Margaret Cancer Centre, University Health Network, Toronto, Canada; Department of Medicine, University of Toronto, Toronto, Canada.

Cancer Cell
|June 15, 2021
PubMed

Insights

Relaxing clinical trial eligibility criteria may improve patient access to new therapies without compromising safety or efficacy. A machine learning approach demonstrated this potential in real-world data, enhancing generalizability.

Area of Science:

  • Clinical Trials
  • Machine Learning
  • Real-World Data Analysis

Background:

  • Patient enrollment in clinical trials is often limited by strict eligibility criteria.
  • These restrictions can hinder access to novel therapeutics and limit the applicability of trial findings.

Purpose of the Study:

  • To investigate the impact of relaxing eligibility criteria on clinical trial outcomes.
  • To assess the safety and efficacy of new therapies with broader patient populations.

Main Methods:

  • A machine learning algorithm was applied to a real-world clinical database.
  • Simulations were performed to evaluate the effects of modified eligibility criteria.

Main Results:

  • Relaxing certain eligibility criteria did not compromise the efficacy or safety of the studied therapies.
  • Broader inclusion criteria showed potential for increased patient access.

Conclusions:

  • Machine learning can identify appropriate modifications to eligibility criteria.
  • Adjusting criteria can improve patient access to innovative treatments and enhance the generalizability of clinical trial results to real-world practice.

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