Model-Based Network Meta-Analysis: A Framework for Evidence Synthesis of Clinical Trial Data

David Mawdsley, Meg Bennetts, Sofia Dias, Martin Boucher, Nicky J. Welton

Research output: Contribution to journalArticlepeer-review


Model-based meta-analysis (MBMA) is increasingly used in drug development to inform decision making and future trial designs, through the use of complex dose and/or time course models. Network Meta-Analysis (NMA) is increasingly being used by reimbursement agencies to estimate a set of coherent relative treatment effects for multiple treatments that respect the randomization within the trials. However, NMAs typically either consider different doses completely independently or lump them together, with few examples of models for dose. We propose a framework, Model Based Network Meta-Analysis (MBNMA), that combines both approaches, that respects randomization, allows estimation and prediction for multiple agents and a range of doses, using plausible physiological dose-response models. We illustrate our approach with an example comparing the efficacies of triptans for migraine relief. This uses a binary endpoint although we note that the model can be easily modified for other outcome types.
Original languageEnglish
Pages (from-to)393-401
Number of pages9
JournalCPT Pharmacometrics & Systems Pharmacology
Issue number8
Publication statusPublished - 1 Aug 2016

Bibliographical note

© 2016 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics


  • Network meta-analysis, model-based meta-analysis, dose-response, drug-development, migraine, triptans

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