Bristol Medical School, University of Bristol, Bristol, UK.
Stat Med. 2024 Nov 30;43(27):5217-5233. doi: 10.1002/sim.10237. Epub 2024 Oct 9.
Network meta-analysis (NMA) combines evidence from multiple trials to compare the effectiveness of a set of interventions. In many areas of 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 article, 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.
网络荟萃分析（NMA）结合了多项试验的证据，以比较一组干预措施的有效性。在许多研究领域，干预措施通常很复杂，由多个组成部分或特征组成。这使得很难定义一组共同的干预措施来进行分析。解决这个问题的一种方法是组件网络荟萃分析（CNMA），它使用荟萃回归框架将每个干预措施定义为组件的子集，其个体效应可以相加。在本文中，我们的动机是对预防儿童肥胖的复杂干预措施进行系统评价。由于试验之间存在很大的异质性，这些干预措施不能表示为组件的子集，而是根据特征的框架进行编码。为了分析这些数据，我们开发了一种定制的 CNMA 启发式模型，使我们能够确定干预措施的最重要特征。我们在三个层面上定义了一个带有协变量的荟萃回归模型：干预、研究和随访时间，以及灵活的交互项。通过为有和没有对照组的试验指定不同的回归结构，我们放宽了之前 CNMA 模型的假设，即对照组是没有干预措施组成部分。此外，我们得出了一种相关结构，该结构考虑了具有多个干预臂和多个随访时间的试验。尽管我们的模型是为肥胖数据集的具体情况开发的，但它具有更广泛的适用性，可以应用于任何可以根据一组共享特征进行编码的复杂干预措施集。
