Individual participant data from digital sources informed and improved precision in the evaluation of predictive biomarkers in Bayesian network meta-analysis.

Objectives: We aimed to develop a network meta-analytic model for the evaluation of treatment effectiveness within predictive biomarker subgroups, by combining evidence from individual participant data (IPD) from digital sources (in the absence of randomized controlled trials) and aggregate data (AD).
Study Design and Setting: A Bayesian framework was developed for modeling time-to-event data to evaluate predictive biomarkers. IPD were sourced from electronic health records, using a target trial emulation approach, or digitized Kaplan-Meier curves. The model is illustrated using two examples: breast cancer with a hormone receptor biomarker, and metastatic colorectal cancer with the Kirsten Rat Sarcoma (KRAS) biomarker.
Results: The model allowed for the estimation of treatment effects in two subgroups of patients defined by their biomarker status. Effectiveness of taxanes did not differ in hormone receptor positive and negative breast cancer patients. Epidermal growth factor receptor inhibitors were more effective than chemotherapy in KRAS wild type colorectal cancer patients but not in patients with KRAS mutant status. Use of IPD reduced uncertainty of the subgroup-specific treatment effect estimates by up to 49%.
Conclusion: Utilization of IPD allowed for more detailed evaluation of predictive biomarkers and cancer therapies and improved precision of the estimates compared to use of AD alone.
Competing Interests: Declaration of competing interest S.B. is a member of the NICE Decision Support Unit (DSU). She has served as a paid consultant, providing methodological advice, to NICE, Roche, RTI Health Solutions and IQVIA, has received payments for educational events from Roche and has received research funding from European Federation of Pharmaceutical Industries & Associations (EEPIA) and Johnson & Johnson. R.K.O. is a member of the National Institute for Health and Care Excellence (NICE) Technology Appraisal Committee, member of the NICE Decision Support Unit (DSU), and associate member of the NICE Technical Support Unit (TSU). She has served as a paid consultant providing unrelated methodological advice to AstraZeneca, Cogentia Healthcare Ltd, Daiichi Sankyo, NICE, Norwegian Institute of Public Health, Roche, and Vifor Pharma. She reports teaching fees from the Association of British Pharmaceutical Industry (ABPI) and the University of Bristol. K.R.A. is a member of the National Institute for Health and Care Excellence (NICE) Diagnostics Advisory Committee, member of the NICE Decision Support Unit (DSU), and the NICE Technical Support Unit (TSU). He has served as a paid consultant, providing unrelated methodological advice to Abbvie, AstraZeneca, Bayer, Bristol-Meyers Squibb, Medtronic, NICE/DHSC, Novartis, Pfizer, and Roche, and has received research funding from Bayer, Association of the British Pharmaceutical Industry (ABPI), European Federation of Pharmaceutical Industries & Associations (EFPIA), Pfizer, Sanofi, and Swiss Precision Diagnostics. He is a Partner and Director of Visible Analytics Limited, a Health Technology Assessment (HTA) consultancy company. P.T. is a member of the National Institute for Health and Care Excellence (NICE) Technology Appraisal Committee, and a member of the NICE Decision Support Unit (DSU). He reports teaching fees from the Association of British Pharmaceutical Industry (ABPI).
(Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)