Evaluating the robustness of an AI pathfinder application on eligibility criteria in multiple myeloma trials using real-world data and historical trials.

ABSTRACT: Background: Eligibility criteria are pivotal in achieving clinical trial success, enabling targeted patient enrollment while ensuring the trial safety. However, overly restrictive criteria hinder enrollment and study result generalizability. Broadening eligibility criteria enhances the trial inclusivity, diversity and enrollment pace. Liu et al. proposed an AI pathfinder method leveraging real-world data to broaden criteria without compromising efficacy and safety outcomes, demonstrating promise in non-small cell lung cancer trials. Aim: To assess the robustness of the methodology, considering diverse qualities of real-world data and to promote its application. Materials/Methods: We revised the AI pathfinder method, applied it to relapsed and refractory multiple

SUBMITTER: Jreich R 

PROVIDER: S-EPMC11225521 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature