Modelling time-course relationships with multiple treatments: Model-based network meta-analysis for continuous summary outcomes

摘要

BackgroundModelbased metaanalysis (MBMA) is increasingly used to inform drugdevelopment decisions by synthesising results from multiple studies to estimate treatment, doseresponse, and timecourse characteristics. Network metaanalysis (NMA) is used in Health Technology Appraisals for simultaneously comparing effects of multiple treatments, to inform reimbursement decisions. Recently, a framework for doseresponse modelbased network metaanalysis (MBNMA) has been proposed that combines, often nonlinear, MBMA modelling with the statistically robust properties of NMA. Here, we aim to extend this framework to timecourse models.MethodsWe propose a Bayesian timecourse MBNMA modelling framework for continuous summary outcomes that allows for nonlinear modelling of multiparameter timecourse functions, accounts for residual correlation between observations, preserves randomisation by modelling relative effects, and allows for testing of inconsistency between direct and indirect evidence on the timecourse parameters. We demonstrate our modelling framework using an illustrative dataset of 23 trials investigating treatments for pain in osteoarthritis.ResultsOf the timecourse functions that we explored, the Emax model gave the best fit to the data and has biological plausibility. Some simplifying assumptions were needed to identify the ET50, due to few observations at early followup times. Treatment estimates were robust to the inclusion of correlations in the likelihood.ConclusionsTimecourse MBNMA provides a statistically robust framework for synthesising evidence on multiple treatments at multiple time points. The use of placebocontrolled studies in drugdevelopment means there is limited potential for inconsistency. The methods can inform drugdevelopment decisions and provide the rigour needed in the reimbursement decisionmaking process.