Assessing the Impact of Model Assumptions in Network Meta-Regression: A Simulation Study

Network meta-regression (NMR) extends network meta-analysis (NMA) by synthesizing evidence on multiple treatments while adjusting for potential effect modifiers. By accounting for effect modification, NMR can reduce between-study heterogeneity and improve the validity of relative treatment effects, providing insight regarding characteristics impacting treatment performance. However, choosing between available NMR models is complex, as each model addresses a similar, but unique research question, and the performance of available NMR models under varying network structures, between-study heterogeneity, and interaction assumptions remains unclear. We evaluated the consequences of model misspecification in a simulation study of 120 evidence-network scenarios designed to reflect potential complications in evidence networks introduced by trial design, heterogeneity levels, and interaction assumptions. We compared the standard interaction-free NMA model with four NMR parameterizations differing in across-comparison interaction assumptions (common vs. independent interactions) and interaction consistency assumptions (with or without consistency). Standard NMA models generally overestimated treatment effects when effect modification was present. NMR models with independent across-comparison interactions maintained appropriate confidence interval coverage in dense networks generated with their corresponding consistency assumptions. However, their coverage deteriorated in sparse networks with between-study heterogeneity. Models assuming consistent interactions are advantageous in networks with multi-arm studies. Ignoring effect modification in NMA can lead to biased treatment effect estimates. When effect modification is anticipated, thoughtful alignment between network structure and NMR assumptions can reduce bias and misleading precision, supporting more reliable medical decision making.