Plea for routinely presenting prediction intervals in meta-analysis

Evaluating the variation in the strength of the effect across studies is a key feature of meta-analyses. This variability is reflected by measures like τ(2) or I(2), but their clinical interpretation is not straightforward. A prediction interval is less complicated: it presents the expected range of...

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Vydáno v:BMJ open Ročník 6; číslo 7; s. e010247
Hlavní autoři: IntHout, Joanna, Ioannidis, John P A, Rovers, Maroeska M, Goeman, Jelle J
Médium: Journal Article
Jazyk:angličtina
Vydáno: England BMJ Publishing Group LTD 12.07.2016
BMJ Publishing Group
Témata:
Estimates
Humans
Meta-analysis
Meta-Analysis as Topic
Polyps
Probability
Publishing
Research Methods
Research Report
Steroids
Heterogeneity
Clinical trial
Random effects
Cochrane Database of Systematic Reviews
Meta-analysis
Prediction interval
ISSN:2044-6055, 2044-6055
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Shrnutí:Evaluating the variation in the strength of the effect across studies is a key feature of meta-analyses. This variability is reflected by measures like τ(2) or I(2), but their clinical interpretation is not straightforward. A prediction interval is less complicated: it presents the expected range of true effects in similar studies. We aimed to show the advantages of having the prediction interval routinely reported in meta-analyses. We show how the prediction interval can help understand the uncertainty about whether an intervention works or not. To evaluate the implications of using this interval to interpret the results, we selected the first meta-analysis per intervention review of the Cochrane Database of Systematic Reviews Issues 2009-2013 with a dichotomous (n=2009) or continuous (n=1254) outcome, and generated 95% prediction intervals for them. In 72.4% of 479 statistically significant (random-effects p<0.05) meta-analyses in the Cochrane Database 2009-2013 with heterogeneity (I(2)>0), the 95% prediction interval suggested that the intervention effect could be null or even be in the opposite direction. In 20.3% of those 479 meta-analyses, the prediction interval showed that the effect could be completely opposite to the point estimate of the meta-analysis. We demonstrate also how the prediction interval can be used to calculate the probability that a new trial will show a negative effect and to improve the calculations of the power of a new trial. The prediction interval reflects the variation in treatment effects over different settings, including what effect is to be expected in future patients, such as the patients that a clinician is interested to treat. Prediction intervals should be routinely reported to allow more informative inferences in meta-analyses.
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ISSN:2044-6055
2044-6055
DOI:10.1136/bmjopen-2015-010247