Evidence Synthesis for Complex Interventions Using Meta-Regression Models

Abstract A goal of evidence synthesis for trials of complex interventions is to inform the design or implementation of novel versions of complex interventions by predicting expected outcomes with each intervention version. Conventional aggregate data meta-analyses of studies comparing complex interv...

Full description

Saved in:
Bibliographic Details
Published in:American journal of epidemiology Vol. 193; no. 2; pp. 323 - 338
Main Authors: Konnyu, Kristin J, Grimshaw, Jeremy M, Trikalinos, Thomas A, Ivers, Noah M, Moher, David, Dahabreh, Issa J
Format: Journal Article
Language:English
Published: United States Oxford University Press 05.02.2024
Subjects:
meta-analysis
multicomponent interventions
hierarchical models
complex interventions
meta-regression
ISSN:0002-9262, 1476-6256, 1476-6256
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract A goal of evidence synthesis for trials of complex interventions is to inform the design or implementation of novel versions of complex interventions by predicting expected outcomes with each intervention version. Conventional aggregate data meta-analyses of studies comparing complex interventions have limited ability to provide such information. We argue that evidence synthesis for trials of complex interventions should forgo aspirations of estimating causal effects and instead model the response surface of study results to 1) summarize the available evidence and 2) predict the average outcomes of future studies or in new settings. We illustrate this modeling approach using data from a systematic review of diabetes quality improvement (QI) interventions involving at least 1 of 12 QI strategy components. We specify a series of meta-regression models to assess the association of specific components with the posttreatment outcome mean and compare the results to conventional meta-analysis approaches. Compared with conventional approaches, modeling the response surface of study results can better reflect the associations between intervention components and study characteristics with the posttreatment outcome mean. Modeling study results using a response surface approach offers a useful and feasible goal for evidence synthesis of complex interventions that rely on aggregate data.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0002-9262
1476-6256
1476-6256
DOI:10.1093/aje/kwad184