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J Clin Epidemiol. 2019 Mar;107:101-106. doi: 10.1016/j.jclinepi.2018.11.023. Epub 2018 Dec 5.

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

Author information

1
Day Treatment Unit, Shalvata Mental Health Centre, Hod-Hsharon, Israel; Department of Psychiatry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. Electronic address: guyatt@mcmaster.ca.
2
Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel.
3
Department of Psychiatry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Addiction Medicine and Dual Disorders Clinic, Lev-Hasharon Medical Centre, Tsur Moshe, Israel.
4
Department of Psychiatry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Division of Psychiatry, The Chaim Sheba Medical Centre, Tel-Hashomer, Israel; Department of Epidemiology and Preventive Medicine, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
5
Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.

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.

KEYWORDS:

GRADE; Heterogeneity; I² statistic, between study variance; Meta-analyses; Random-effects models; Systematic reviews

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