Network meta-analysis (NMA) combines evidence from multiple trials to compare the effectiveness of a set of interventions. In public health research, interventions are often complex, made up of multiple components or features. This makes it difficult to define a common set of interventions on which to perform the analysis. One approach to this problem is component network meta-analysis (CNMA) which uses a meta-regression framework to define each intervention as a subset of components whose individual effects combine additively. In this paper, we are motivated by a systematic review of complex interventions to prevent obesity in children. Due to considerable heterogeneity across the trials, these interventions cannot be expressed as a subset of components but instead are coded against a framework of characteristic features. To analyse these data, we develop a bespoke CNMA-inspired model that allows us to identify the most important features of interventions. We define a meta-regression model with covariates on three levels: intervention, study, and follow-up time, as well as flexible interaction terms. By specifying different regression structures for trials with and without a control arm, we relax the assumption from previous CNMA models that a control arm is the absence of intervention components. Furthermore, we derive a correlation structure that accounts for trials with multiple intervention arms and multiple follow-up times. Although our model was developed for the specifics of the obesity data set, it has wider applicability to any set of complex interventions that can be coded according to a set of shared features.

翻译：网络荟萃分析（NMA）整合来自多个试验的证据，以比较一组干预措施的有效性。在公共卫生研究中，干预措施通常较为复杂，由多个组成部分或特征构成。这使得定义一组通用的干预措施进行分析变得困难。针对该问题的一种方法是成分网络荟萃分析（CNMA），它采用元回归框架将每种干预措施定义为各成分子集，其个体效应可加性组合。本文的研究动机源于一项针对儿童肥胖预防的复杂干预措施的系统综述。由于试验间存在显著异质性，这些干预措施无法表示为成分子集，而是根据一组特征性框架进行编码。为分析这些数据，我们开发了一种定制化的CNMA启发式模型，能够识别干预措施中最重要的特征。我们定义了一个包含三层协变量的元回归模型：干预层、研究层和随访时间层，并引入灵活的交互项。通过为含对照组和无对照组的试验指定不同回归结构，我们放宽了先前CNMA模型中“对照组即为无干预成分”的假设。此外，我们推导出一种相关性结构，以处理具有多个干预臂和多个随访时间的试验。尽管该模型是针对肥胖数据集的特定需求开发的，但它可广泛适用于任何能够根据一组共享特征进行编码的复杂干预措施集合。