A Complex Meta-Regression Model to Identify Effective Features of Interventions From Multi-Arm, Multi-Follow-Up Trials.

Autor: Davies AL; Bristol Medical School, University of Bristol, Bristol, UK., Higgins JPT; Bristol Medical School, University of Bristol, Bristol, UK.
Jazyk: angličtina
Zdroj: Statistics in medicine [Stat Med] 2024 Nov 30; Vol. 43 (27), pp. 5217-5233. Date of Electronic Publication: 2024 Oct 09.
DOI: 10.1002/sim.10237
Abstrakt: 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.
(© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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