Model-based network meta-analysis: Joint estimation of dose-response and time-course relationships

Under the network meta-analysis (NMA) framework for aggregate data, there are limited possibilities for evidence synthesis across multiple treatment doses or timepoints. Model-based network meta-analysis (MBNMA) has been recommended as a framework for either evidence synthesis across multiple dose levels or across multiple timepoints to circumvent the limitations of the NMA. A joint dose-response and time-course MBNMA (DT-MBNMA) is proposed that combines the strengths of both DT-MBNMA. This framework allows for combining data at multiple timepoints from studies in early clinical development with a broad range of doses to late-stage clinical studies with a limited range of doses. The method respects randomization and allows for assessment of consistency and hence satisfies the requirements from reimbursement agencies. The method was validated in a simulation study, showing that the drug effect parameters and therefore indirect treatment effects could be recovered without bias, while the precision of the treatment effects was dependent on the simulated network. Compared with a standard NMA, the methodology increased the statistical efficiency of the indirect treatment comparison (ITC). The use of the method was further illustrated on a dataset consisting of seven randomized clinical trials (RCTs) (26 treatment arms) in the treatment of obesity with Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), with a broad range of doses and follow-up times. The method integrated phase 2 and 3 data seamlessly into the meta-analysis and provided greater precision on the treatment effects compared to NMA. Finally, the statistical framework may be used to support clinical decision-making, providing a framework for ITC during drug development.