Evaluating eligibility criteria of oncology trials using real-world data and AI
Ruishan Liu1, Shemra Rizzo2, Samuel Whipple2
1Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
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.
Insights
Broadening restrictive eligibility criteria in cancer clinical trials can double patient eligibility and potentially improve survival outcomes. This data-driven approach enhances trial inclusivity while ensuring patient safety.
Area of Science:
- Oncology
- Clinical Trial Design
- Health Informatics
Background:
- Clinical trials face challenges in patient inclusivity due to restrictive eligibility criteria.
- Designing inclusive cancer trials is crucial for broader patient representation and generalizable results.
Purpose of the Study:
- To systematically evaluate the impact of eligibility criteria on cancer trial populations and outcomes.
- To assess the effect of common laboratory value exclusions on trial hazard ratios.
- To explore a data-driven approach for broadening restrictive criteria to enhance patient eligibility.
Main Methods:
- Utilized the Trial Pathfinder computational framework to emulate completed advanced non-small-cell lung cancer trials.
- Analyzed real-world data from a nationwide electronic health records database (61,094 patients).
- Employed a data-driven methodology to broaden restrictive criteria and evaluate changes in eligible patient populations and survival outcomes.
Main Results:
- Many common eligibility criteria, including laboratory value exclusions, had minimal impact on trial hazard ratios.
- Broadening restrictive criteria more than doubled the pool of eligible patients on average.
- Broadening criteria led to an average decrease of 0.05 in the hazard ratio for overall survival.
Conclusions:
- Restrictive eligibility criteria may exclude patients who could benefit from cancer treatments.
- A data-driven approach to evaluating and broadening criteria can significantly increase trial inclusivity.
- This methodology facilitates the design of more inclusive clinical trials without compromising patient safety.
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