TY - JOUR
T1 - Getting more out of meta-analyses
T2 - a new approach to meta-analysis in light of unexplained heterogeneity
AU - Saad, Amit
AU - Yekutieli, Daniel
AU - Lev-Ran, Shaul
AU - Gross, Raz
AU - Guyatt, Gordon
N1 - Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2019/3
Y1 - 2019/3
N2 - 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.
AB - 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.
KW - GRADE
KW - Heterogeneity
KW - I² statistic, between study variance
KW - Meta-analyses
KW - Random-effects models
KW - Systematic reviews
UR - http://www.scopus.com/inward/record.url?scp=85059140575&partnerID=8YFLogxK
U2 - 10.1016/j.jclinepi.2018.11.023
DO - 10.1016/j.jclinepi.2018.11.023
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C2 - 30529650
AN - SCOPUS:85059140575
SN - 0895-4356
VL - 107
SP - 101
EP - 106
JO - Journal of Clinical Epidemiology
JF - Journal of Clinical Epidemiology
ER -