Getting more out of meta-analyses: a new approach to meta-analysis in light of unexplained heterogeneity.

Abstract:

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.

journal_name

J Clin Epidemiol

authors

Saad A,Yekutieli D,Lev-Ran S,Gross R,Guyatt G

doi

10.1016/j.jclinepi.2018.11.023

subject

Has Abstract

pub_date

2019-03-01 00:00:00

pages

101-106

eissn

0895-4356

issn

1878-5921

pii

S0895-4356(18)30597-3

journal_volume

107

pub_type

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