Literature DB >> 34550595

Modeling Multicomponent Interventions in Network Meta-Analysis.

Areti Angeliki Veroniki1,2, Georgios Seitidis3, Stavros Nikolakopoulos3, Marta Ballester4,5,6, Jessica Beltran7, Monique Heijmans8, Dimitris Mavridis3,9.   

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.
© 2022. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Additive effects; Combination therapies; Complex interventions; Component network meta-analysis; Multiple treatment meta-analysis; Self-management interventions

Mesh:

Year:  2022        PMID: 34550595     DOI: 10.1007/978-1-0716-1566-9_15

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  1 in total

1.  Stability of additive treatment effects in multiple treatment comparison meta-analysis: a simulation study.

Authors:  Kristian Thorlund; Edward Mills
Journal:  Clin Epidemiol       Date:  2012-04-16       Impact factor: 4.790

  1 in total

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