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：在网络荟萃分析中，显式建模研究间异质性对于确保有效推断和避免精度夸大至关重要。虽然加性随机效应模型是常规方法，但乘法效应模型仍未得到充分探索。该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，产生与固定效应模型相同的点估计值，同时扩大置信区间。我们基于nma数据库中的异质性显著的双臂研究网络荟萃分析，通过赤池信息准则评估模型拟合度，对随机效应模型和乘法效应模型进行了实证比较。乘法效应模型常表现出与随机效应模型相当或更优的拟合效果。案例研究进一步揭示，随机效应模型对极端和不精确观测值敏感，而乘法效应模型对此类观测值赋予较小权重，因而对发表偏倚具有更强的稳健性。我们的结果表明，在网络荟萃分析实践中，乘法效应模型值得与常规随机效应模型共同纳入考量。