Modelling time‐course relationships with multiple treatments: Model‐based network meta‐analysis for continuous summary outcomes
Year: 2019
Type: article
Abstract: BACKGROUND: Model-based meta-analysis (MBMA) is increasingly used to inform drug-development decisions by synthesising results from multiple studies to estimate treatment, dose-response, and time-course characteristics. Network meta-analysis (NMA) is used in Health Technology Appraisals for simultaneously comparing effects of multiple treatments, to inform reimbursement decisions. Recently, a framework for dose-response model-based network meta-analysis (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 time-course models. METHODS: We propose a Bayesian time-course MBNMA modelling framework for continuous summary outcomes that allows for nonlinear modelling of multiparameter time-course 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 time-course parameters. We demonstrate our modelling framework using an illustrative dataset of 23 trials investigating treatments for pain in osteoarthritis. RESULTS: , due to few observations at early follow-up times. Treatment estimates were robust to the inclusion of correlations in the likelihood. CONCLUSIONS: Time-course MBNMA provides a statistically robust framework for synthesising evidence on multiple treatments at multiple time points. The use of placebo-controlled studies in drug-development means there is limited potential for inconsistency. The methods can inform drug-development decisions and provide the rigour needed in the reimbursement decision-making process.
Source: Research Synthesis Methods
DOI: 10.1002/jrsm.1351
Institutions: University of Bristol, Pfizer (United Kingdom)
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $2,750
Awards: Functional relationships in Bayesian evidence synthesis of multiple outcomes, G1670097818, The MRC ConDuCT-II Hub. COllaboration and iNnovation in DiffiUlt and Complex randomised controlled Trials In Invasive procedures, MICA: Model Based Network Meta-Analysis for Pharmacometrics and Drug-Development, G7400372156, G827581813
OpenAlex ID: W2941402878