Explicit modelling of between-study heterogeneity is essential in network meta-analysis (NMA) to ensure valid inference and avoid overstating precision. While the additive random-effects (RE) model is the conventional approach, the multiplicative-effect (ME) model remains underexplored. The ME model inflates within-study variances by a common factor estimated via weighted least squares, yielding identical point estimates to a fixed-effect model while inflating confidence intervals. We empirically compared RE and ME models across NMAs of two-arm studies with significant heterogeneity from the nmadb database, assessing model fit using the Akaike Information Criterion. The ME model often provided comparable or better fit to the RE model. Case studies further revealed that RE models are sensitive to extreme and imprecise observations, whereas ME models assign less weight to such observations and hence exhibit greater robustness to publication bias. Our results suggest that the ME model warrant consideration alongside conventional RE model in NMA practice.

翻译：对研究间异质性进行显式建模对于网络荟萃分析（NMA）至关重要，这有助于确保推断的有效性并避免过度渲染精确性。尽管加性随机效应（RE）模型是传统方法，但乘性效应（ME）模型仍未得到充分探索。ME模型通过加权最小二乘法估计的共同因子放大研究内方差，在扩大置信区间的同时，得出与固定效应模型相同的点估计值。我们基于nmadb数据库中具有显著异质性的双组研究数据，通过实证比较了RE与ME模型在NMA中的表现，并使用赤池信息准则（AIC）评估模型拟合度。结果表明，ME模型常能提供与RE模型相当甚至更优的拟合效果。案例研究进一步揭示，RE模型对极端且不精确的观测值敏感，而ME模型对此类观测值赋予较低权重，因此对发表偏倚具有更强的稳健性。我们的研究结论表明，在NMA实践中，应将ME模型与传统RE模型一并纳入考量。