A fully Bayesian application of the Copas selection model for publication bias extended to network meta-analysis
The Copas parametric model is aimed at exploring the potential impact of publication bias via sensitivity analysis, by making assumptions regarding the probability of publication of individual studies related to the standard error of their effect sizes. Reviewers often have prior assumptions about t...
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| Published in | Statistics in medicine Vol. 32; no. 1; pp. 51 - 66 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Chichester, UK
John Wiley & Sons, Ltd
15.01.2013
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
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| Abstract | The Copas parametric model is aimed at exploring the potential impact of publication bias via sensitivity analysis, by making assumptions regarding the probability of publication of individual studies related to the standard error of their effect sizes. Reviewers often have prior assumptions about the extent of selection in the set of studies included in a meta‐analysis. However, a Bayesian implementation of the Copas model has not been studied yet. We aim to present a Bayesian selection model for publication bias and to extend it to the case of network meta‐analysis where each treatment is compared either with placebo or with a reference treatment creating a star‐shaped network. We take advantage of the greater flexibility offered in the Bayesian context to incorporate in the model prior information on the extent and strength of selection. To derive prior distributions, we use both external data and an elicitation process of expert opinion. Copyright © 2012 John Wiley & Sons, Ltd. |
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| AbstractList | The Copas parametric model is aimed at exploring the potential impact of publication bias via sensitivity analysis, by making assumptions regarding the probability of publication of individual studies related to the standard error of their effect sizes. Reviewers often have prior assumptions about the extent of selection in the set of studies included in a meta-analysis. However, a Bayesian implementation of the Copas model has not been studied yet. We aim to present a Bayesian selection model for publication bias and to extend it to the case of network meta-analysis where each treatment is compared either with placebo or with a reference treatment creating a star-shaped network. We take advantage of the greater flexibility offered in the Bayesian context to incorporate in the model prior information on the extent and strength of selection. To derive prior distributions, we use both external data and an elicitation process of expert opinion. The Copas parametric model is aimed at exploring the potential impact of publication bias via sensitivity analysis, by making assumptions regarding the probability of publication of individual studies related to the standard error of their effect sizes. Reviewers often have prior assumptions about the extent of selection in the set of studies included in a meta‐analysis. However, a Bayesian implementation of the Copas model has not been studied yet. We aim to present a Bayesian selection model for publication bias and to extend it to the case of network meta‐analysis where each treatment is compared either with placebo or with a reference treatment creating a star‐shaped network. We take advantage of the greater flexibility offered in the Bayesian context to incorporate in the model prior information on the extent and strength of selection. To derive prior distributions, we use both external data and an elicitation process of expert opinion. Copyright © 2012 John Wiley & Sons, Ltd. The Copas parametric model is aimed at exploring the potential impact of publication bias via sensitivity analysis, by making assumptions regarding the probability of publication of individual studies related to the standard error of their effect sizes. Reviewers often have prior assumptions about the extent of selection in the set of studies included in a meta-analysis. However, a Bayesian implementation of the Copas model has not been studied yet. We aim to present a Bayesian selection model for publication bias and to extend it to the case of network meta-analysis where each treatment is compared either to placebo or to a reference treatment creating a star-shaped network. We take advantage of the greater flexibility offered in the Bayesian context to incorporate in the model prior information on the extent and strength of selection. To derive prior distributions, we use both external data and an elicitation process of expert opinion. The Copas parametric model is aimed at exploring the potential impact of publication bias via sensitivity analysis, by making assumptions regarding the probability of publication of individual studies related to the standard error of their effect sizes. Reviewers often have prior assumptions about the extent of selection in the set of studies included in a meta-analysis. However, a Bayesian implementation of the Copas model has not been studied yet. We aim to present a Bayesian selection model for publication bias and to extend it to the case of network meta-analysis where each treatment is compared either with placebo or with a reference treatment creating a star-shaped network. We take advantage of the greater flexibility offered in the Bayesian context to incorporate in the model prior information on the extent and strength of selection. To derive prior distributions, we use both external data and an elicitation process of expert opinion.The Copas parametric model is aimed at exploring the potential impact of publication bias via sensitivity analysis, by making assumptions regarding the probability of publication of individual studies related to the standard error of their effect sizes. Reviewers often have prior assumptions about the extent of selection in the set of studies included in a meta-analysis. However, a Bayesian implementation of the Copas model has not been studied yet. We aim to present a Bayesian selection model for publication bias and to extend it to the case of network meta-analysis where each treatment is compared either with placebo or with a reference treatment creating a star-shaped network. We take advantage of the greater flexibility offered in the Bayesian context to incorporate in the model prior information on the extent and strength of selection. To derive prior distributions, we use both external data and an elicitation process of expert opinion. The Copas parametric model is aimed at exploring the potential impact of publication bias via sensitivity analysis, by making assumptions regarding the probability of publication of individual studies related to the standard error of their effect sizes. Reviewers often have prior assumptions about the extent of selection in the set of studies included in a meta-analysis. However, a Bayesian implementation of the Copas model has not been studied yet. We aim to present a Bayesian selection model for publication bias and to extend it to the case of network meta-analysis where each treatment is compared either with placebo or with a reference treatment creating a star-shaped network. We take advantage of the greater flexibility offered in the Bayesian context to incorporate in the model prior information on the extent and strength of selection. To derive prior distributions, we use both external data and an elicitation process of expert opinion. [PUBLICATION ABSTRACT] |
| Author | Cipriani, Andrea Salanti, Georgia Mavridis, Dimitris Sutton, Alex |
| AuthorAffiliation | 4 Department of Public Health and Community Medicine, University of Verona 1 Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece 5 Department of Psychiatry, University of Oxford 3 Department of Health Sciences, University of Leicester 2 Department of Primary Education, University of Ioannina |
| AuthorAffiliation_xml | – name: 3 Department of Health Sciences, University of Leicester – name: 5 Department of Psychiatry, University of Oxford – name: 2 Department of Primary Education, University of Ioannina – name: 1 Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece – name: 4 Department of Public Health and Community Medicine, University of Verona |
| Author_xml | – sequence: 1 givenname: Dimitris surname: Mavridis fullname: Mavridis, Dimitris email: Dimitris Mavridis, Department of Hygiene and Epidemiology, University of Ioannina, School of Medicine, Ioannina, Greece., dimi.mavridis@googlemail.com organization: Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece – sequence: 2 givenname: Alex surname: Sutton fullname: Sutton, Alex organization: Department of Health Sciences, University of Leicester, Leicester, U.K – sequence: 3 givenname: Andrea surname: Cipriani fullname: Cipriani, Andrea organization: Department of Public Health and Community Medicine, University of Verona, Verona, Italy – sequence: 4 givenname: Georgia surname: Salanti fullname: Salanti, Georgia organization: Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece |
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| References_xml | – reference: Egger M, Smith GD, Scheider M, Minder C.Bias in meta analysis detected by a simple graphical test. British Medical Journal 1997; 315:629-634. – reference: Moreno SG, Sutton AJ, Ades AE, Cooper NJ, Abrams KR.Adjusting for publication biases across similar interventions performed well when compared with gold standard data. Journal of Clinical Epidemiology 2011; 64:1230-1241. – reference: Whittington CJ, Kendall T, Fonagy P, Cottrel D, Cotgrove A, Boddington E.Selective serotonin reuptake inhibitors in childhood depression: systematic review of published versus unpublished data. Lancet 2004; 363:1341-1345. – reference: Peters JL, Sutton AJ, Jones DR, Abrams KR, Rushton L, Moreno SG.Assessing publication bias in meta analyses in the presence of between-study heterogeneity. Journal of the Royal Statistical Society, Seies A 2010; 173:575-591. – reference: Copas JB.What works? Selectivity models and meta-analysis. 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| SubjectTerms | antidepressants Antidepressive Agents - therapeutic use Bayes Theorem Bayesian analysis Bias Depression - drug therapy Humans informative priors Medical treatment Meta-analysis Meta-Analysis as Topic mixed treatment comparison Models, Statistical multiple treatments prior elicitation Probability distribution Publication Bias small study effects |
| Title | A fully Bayesian application of the Copas selection model for publication bias extended to network meta-analysis |
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