Getting more out of meta-analyses: a new approach to meta-analysis in light of unexplained heterogeneity.
GRADE
Heterogeneity
I² statistic, between study variance
Meta-analyses
Random-effects models
Systematic reviews
Journal
Journal of clinical epidemiology
ISSN: 1878-5921
Titre abrégé: J Clin Epidemiol
Pays: United States
ID NLM: 8801383
Informations de publication
Date de publication:
03 2019
03 2019
Historique:
received:
26
07
2018
revised:
23
10
2018
accepted:
30
11
2018
pubmed:
12
12
2018
medline:
28
2
2020
entrez:
12
12
2018
Statut:
ppublish
Résumé
Meta-analyses sometimes summarize results in the presence of substantial unexplained between-study heterogeneity. As GRADE criteria highlight, unexplained heterogeneity reduces certainty in the evidence, resulting in limited confidence in average effect estimates. The aim of this paper is to provide a new clinically useful approach to estimating an intervention effect in light of unexplained heterogeneity. We used a random-effects model to estimate the distribution of an intervention-effect across various groups of patients given data derived from meta-analysis. The model provides a distribution of the probabilities of various possible effects in a new group of patients. We examined how our method influenced the conclusions of two meta-analyses. In one example, our method illustrated that evidence from a meta-analysis did not support authors' highly publicized conclusion that hypericum is as effective as other antidepressants. In the second example, our method provided insight into a subgroup analysis of the effect of ribavirin in hepatitis C, demonstrating clear important benefit in one subgroup but not in others. Analysing the distribution of an intervention-effect in random-effects models may enable clinicians to improve their understanding of the probability of particular-intervention effects in a new population.
Sections du résumé
BACKGROUND AND OBJECTIVES
Meta-analyses sometimes summarize results in the presence of substantial unexplained between-study heterogeneity. As GRADE criteria highlight, unexplained heterogeneity reduces certainty in the evidence, resulting in limited confidence in average effect estimates. The aim of this paper is to provide a new clinically useful approach to estimating an intervention effect in light of unexplained heterogeneity.
METHODS
We used a random-effects model to estimate the distribution of an intervention-effect across various groups of patients given data derived from meta-analysis. The model provides a distribution of the probabilities of various possible effects in a new group of patients. We examined how our method influenced the conclusions of two meta-analyses.
RESULTS
In one example, our method illustrated that evidence from a meta-analysis did not support authors' highly publicized conclusion that hypericum is as effective as other antidepressants. In the second example, our method provided insight into a subgroup analysis of the effect of ribavirin in hepatitis C, demonstrating clear important benefit in one subgroup but not in others.
CONCLUSION
Analysing the distribution of an intervention-effect in random-effects models may enable clinicians to improve their understanding of the probability of particular-intervention effects in a new population.
Identifiants
pubmed: 30529650
pii: S0895-4356(18)30597-3
doi: 10.1016/j.jclinepi.2018.11.023
pii:
doi:
Substances chimiques
Antidepressive Agents
0
Plant Extracts
0
Ribavirin
49717AWG6K
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
101-106Informations de copyright
Copyright © 2018 Elsevier Inc. All rights reserved.