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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Bibliographic Details
Published in:	BMJ open Vol. 6; no. 7; p. e010247
Main Authors:	IntHout, Joanna, Ioannidis, John P A, Rovers, Maroeska M, Goeman, Jelle J
Format:	Journal Article
Language:	English
Published:	England BMJ Publishing Group LTD 12.07.2016 BMJ Publishing Group
Subjects:	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
Online Access:	Get full text
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Abstract	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.
AbstractList	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. OBJECTIVESEvaluating 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.DESIGNWe 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.RESULTSIn 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.CONCLUSIONSThe 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. ObjectivesEvaluating the variation in the strength of the effect across studies is a key feature of meta-analyses. This variability is reflected by measures like τ2 or I2, 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.DesignWe 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.ResultsIn 72.4% of 479 statistically significant (random-effects p<0.05) meta-analyses in the Cochrane Database 2009–2013 with heterogeneity (I2>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.ConclusionsThe 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.
Author	Goeman, Jelle J IntHout, Joanna Ioannidis, John P A Rovers, Maroeska M
AuthorAffiliation	4 Department of Statistics , Stanford University School of Humanities and Sciences , Stanford, California , USA 2 Department of Medicine , Stanford Prevention Research Center, Stanford University School of Humanities and Sciences , Stanford, California , USA 1 Radboud University Medical Center, Radboud Institute for Health Sciences (RIHS) , Nijmegen , The Netherlands 3 Department of Health Research and Policy , Stanford University School of Medicine , Stanford, California , USA 5 Meta-Research Innovation Center at Stanford (METRICS), Stanford University , Stanford, California , USA
AuthorAffiliation_xml	– name: 1 Radboud University Medical Center, Radboud Institute for Health Sciences (RIHS) , Nijmegen , The Netherlands – name: 3 Department of Health Research and Policy , Stanford University School of Medicine , Stanford, California , USA – name: 4 Department of Statistics , Stanford University School of Humanities and Sciences , Stanford, California , USA – name: 5 Meta-Research Innovation Center at Stanford (METRICS), Stanford University , Stanford, California , USA – name: 2 Department of Medicine , Stanford Prevention Research Center, Stanford University School of Humanities and Sciences , Stanford, California , USA
Author_xml	– sequence: 1 givenname: Joanna surname: IntHout fullname: IntHout, Joanna – sequence: 2 givenname: John P A surname: Ioannidis fullname: Ioannidis, John P A – sequence: 3 givenname: Maroeska M surname: Rovers fullname: Rovers, Maroeska M – sequence: 4 givenname: Jelle J surname: Goeman fullname: Goeman, Jelle J
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/27406637$$D View this record in MEDLINE/PubMed
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Snippet	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... ObjectivesEvaluating the variation in the strength of the effect across studies is a key feature of meta-analyses. This variability is reflected by measures... OBJECTIVESEvaluating the variation in the strength of the effect across studies is a key feature of meta-analyses. This variability is reflected by measures...
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SubjectTerms	Estimates Humans Meta-analysis Meta-Analysis as Topic Polyps Probability Publishing Research Methods Research Report Steroids
Title	Plea for routinely presenting prediction intervals in meta-analysis
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