Standard network meta‐analysis (NMA) and indirect comparisons combine aggregate data from multiple studies on treatments of interest, assuming that any effect modifiers are balanced across populations. Population adjustment methods relax this assumption using individual patient data from one or more studies. However, current matching‐adjusted indirect comparison and simulated treatment comparison methods are limited to pairwise indirect comparisons and cannot predict into a specified target population. Existing meta‐regression approaches incur aggregation bias. We propose a new method extending the standard NMA framework. An individual level regression model is defined, and aggregate data are fitted by integrating over the covariate distribution to form the likelihood. Motivated by the complexity of the closed form integration, we propose a general numerical approach using quasi‐Monte‐Carlo integration. Covariate correlation structures are accounted for by using copulas. Crucially for decision making, comparisons may be provided in any target population with a given covariate distribution. We illustrate the method with a network of plaque psoriasis treatments. Estimated population‐average treatment effects are similar across study populations, as differences in the distributions of effect modifiers are small. A better fit is achieved than a random effects NMA, uncertainty is substantially reduced by explaining within‐ and between‐study variation, and estimates are more interpretable.

中文翻译：

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用于人群调整治疗比较的多级网络元回归。


标准网络荟萃分析 (NMA) 和间接比较结合了有关感兴趣治疗的多项研究的汇总数据，假设任何效应修饰因素在人群之间是平衡的。群体调整方法使用来自一项或多项研究的个体患者数据放宽了这一假设。然而，目前的匹配调整间接比较和模拟治疗比较方法仅限于成对间接比较，无法预测特定的目标人群。现有的元回归方法会产生聚合偏差。我们提出了一种扩展标准 NMA 框架的新方法。定义个体水平回归模型，并通过整合协变量分布来拟合聚合数据以形成可能性。受封闭式积分复杂性的启发，我们提出了一种使用准蒙特卡罗积分的通用数值方法。协变量相关结构通过使用联结函数来解释。对于决策制定至关重要的是，可以在具有给定协变量分布的任何目标人群中进行比较。我们通过斑块型银屑病治疗网络来说明该方法。估计的人群平均治疗效果在研究人群中是相似的，因为效果调节剂的分布差异很小。与随机效应 NMA 相比，可以实现更好的拟合，通过解释研究内和研究间的变异大大减少了不确定性，并且估计值更容易解释。