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Plea for routinely presenting prediction intervals in meta-analysis

BMJ open, 2016-07, Vol.6 (7), p.e010247-e010247 [Peer Reviewed Journal]

Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing ;Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/ 2016 This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/ 2016 ;ISSN: 2044-6055 ;EISSN: 2044-6055 ;DOI: 10.1136/BMJOPEN-2015-010247 ;PMID: 27406637

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  • Title:
    Plea for routinely presenting prediction intervals in meta-analysis
  • Author: IntHout, Joanna ; Ioannidis, John P A ; Rovers, Maroeska M ; Goeman, Jelle J
  • Subjects: Estimates ; Humans ; Meta-analysis ; Meta-Analysis as Topic ; Polyps ; Probability ; Publishing ; Research Methods ; Research Report ; Steroids
  • Is Part Of: BMJ open, 2016-07, Vol.6 (7), p.e010247-e010247
  • Description: 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.
  • Publisher: England: BMJ Publishing Group LTD
  • Language: English
  • Identifier: ISSN: 2044-6055
    EISSN: 2044-6055
    DOI: 10.1136/BMJOPEN-2015-010247
    PMID: 27406637
  • Source: ProQuest One Psychology
    BMJ Open Access Journals
    GFMER Free Medical Journals
    MEDLINE
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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