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

Abstract

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, and 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.