Network meta-analysis (NMA) is widely used to compare multiple interventions simultaneously by synthesizing direct and indirect evidence. The general fixed or random effects contrast-based NMA model can be applied to different outcomes and data structures by opting for either an arm-based or contrast-based likelihood depending on the data available. Depending on the outcome and link-function, we estimate either collapsible or non-collapsible effect measures. Using an illustrative example involving binary outcomes and the non-collapsible odds ratio, we demonstrate that the standard NMA model produces estimates for non-collapsible effect measures that are biased toward the null when studies in the evidence base enroll heterogeneous populations (mixtures of distinct risk groups) that vary across studies. Importantly, this also holds when there are no differences in effect-modifiers across studies; the standard assumption of a common treatment effect when there are no differences in the distribution of effect-modifiers across studies is not appropriate when studies have different baseline risks. As a potential solution, we propose a ``bookend'' approach that explicitly models mixed-population studies as weighted combinations of two homogeneous subpopulations identified from studies with extreme baseline risks and provide guidance for practitioners to determine if bias due to non-collapsibility may be a concern.

翻译：网络Meta分析（NMA）通过综合直接与间接证据，被广泛用于同时比较多种干预措施。通用的基于对比的固定或随机效应NMA模型可通过根据可用数据选择基于臂或基于对比的似然函数，适用于不同结局指标与数据结构。根据结局指标与链接函数，我们估计可压缩或非可压缩的效应度量。通过一个涉及二分类结局与非可压缩比值比的示例，我们证明当证据基础中的研究纳入跨研究存在差异的异质性人群（不同风险群体的混合）时，标准NMA模型对非可压缩效应度量的估计会产生向零值偏倚。重要的是，即使研究间效应修饰因子的分布无差异，此偏倚依然存在；当研究间基线风险不同时，关于效应修饰因子分布无差异则处理效应相同的标准假设并不适用。作为一种潜在解决方案，我们提出一种“书挡”方法，将混合人群研究显式建模为从极端基线风险研究中识别出的两个同质子人群的加权组合，并为实践者提供判断非可压缩性所致偏倚是否需关注的指导。