Supplementary material: Evaluating the robustness of an AI pathfinder application on eligibility criteria in multiple myeloma trials using real-worlddata and historical trials

These are peer-reviewed supplementary materials for the article 'Evaluating the robustness of an AI pathfinder application on eligibility criteria in multiple myeloma trials using real-world data and historical trials' published in the Journal of Comparative Effectiveness Research.

• Supplementary Table 1: Computable eligibility criteria of RRMM clinical trials derived from Flatiron Health and Optum’s EHR.
• Supplementary Table 2: Descriptive statistics of RRMM baseline characteristics in Optum’s EHR and Flatiron Health.
• Supplementary Figure 1: Percentage of patients excluded versus Shapley values by each eligibility criterion for NIMBUS, ENDEAVOR, ELOQUENT2, CASTOR, CANDOR, and TOURMALINE trials using Optum’s EHR and Flatiron Health real-world databases.
• Supplementary Table 3: The number of eligibility criteria (# EC), the number of eligible patients (# Pat.) and the hazard ratio (HR) of the progression-free survival of the original clinical trials and emulated RRMM trials with eligibility criteria under three scenarios using Optum’s EHR database: the original criteria used in the trial, fully relaxed criteria, and robust data-driven criteria.

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 myeloma trials and compared it using two real-world data sources. We modified the assessment and considered a bootstrap confidence interval of the AI pathfinder to enhance the decision robustness. Results & conclusion: Our findings confirmed the AI pathfinder’s potential in identifying certain eligibility criteria, in other words, prior complications and laboratory tests for relaxation or removal. However, a robust quantitative assessment, accounting for trial variability and real-world data quality, is crucial for confident decision-making and prioritizing safety alongside efficacy.