评估网络Meta回归中模型假设的影响：一项模拟研究
Assessing the Impact of Model Assumptions in Network Meta-Regression: A Simulation Study
AI总结：
该模拟研究评估网络Meta回归中模型假设影响，比较标准无交互NMA模型与四种NMR参数化，发现存在效应修饰时标准NMA模型高估效果，不同NMR模型在不同网络结构等下有不同表现，合理匹配可减少偏差，支持可靠医学决策。
AI中文摘要：
网络Meta回归（NMR）通过综合多种治疗的证据并调整潜在效应修饰因素扩展了网络Meta分析（NMA）。选择合适的NMR模型很复杂，不同模型针对相似但独特的研究问题，其在不同网络结构、研究间异质性和交互假设下的性能不明。在120个证据网络场景的模拟研究中评估模型错误指定的后果，比较了标准无交互NMA模型与四种NMR参数化。结果表明，存在效应修饰时标准NMA模型通常高估治疗效果，具有独立跨比较交互的NMR模型在相应一致性假设生成的密集网络中保持适当的置信区间覆盖，但在具有研究间异质性的稀疏网络中覆盖变差，假设一致交互的模型在多臂研究网络中具有优势。忽略NMA中的效应修饰会导致治疗效果估计有偏差，考虑效应修饰时，网络结构与NMR假设的合理匹配可减少偏差和误导性精度，支持更可靠的医学决策。
英文摘要：
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.
