Modeling Multicomponent Interventions in Network Meta-Analysis

Areti Angeliki Veroniki*, Georgios Seitidis, Stavros Nikolakopoulos, Marta Ballester, Jessica Beltran, Monique Heijmans, Dimitris Mavridis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

There is a rapid increase in trials assessing healthcare interventions consisting of a combination of drugs (polytherapies) or multiple components. In the latter type of interventions (also known as complex interventions), the aspect of complexity is of paramount importance. For example, nonpharmacological interventions, such as psychological interventions or self-management interventions, usually share common components that relate to the nature of intervention, who delivers it, or where and how. In a network of trials, there is often the need to identify the most effective (or safest) component and/or combination of components. Four key meta-analytical approaches have been presented in the literature to handle complex interventions. These include (a) the single-effect model, (b) the full interaction model, (c) the additive main effects model, and (d) the two-way interaction model. In this chapter, we present and discuss the advantages and limitations of these approaches. We illustrate these methods using a network that assesses the relative effects of self-management interventions on waist size in patients with type 2 diabetes.

Original languageEnglish
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Pages245-261
Number of pages17
Volume2345
DOIs
Publication statusPublished - 2022
Externally publishedYes

Publication series

NameMethods in Molecular Biology
Volume2345
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

Keywords

  • Additive effects
  • Combination therapies
  • Complex interventions
  • Component network meta-analysis
  • Multiple treatment meta-analysis
  • Self-management interventions
  • Humans
  • Diabetes Mellitus, Type 2
  • Network Meta-Analysis

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