Literature DB >> 20213702

Modelling multiple sources of dissemination bias in meta-analysis.

Jack Bowden1, Dan Jackson, Simon G Thompson.   

Abstract

Asymmetry in the funnel plot for a meta-analysis suggests the presence of dissemination bias. This may be caused by publication bias through the decisions of journal editors, by selective reporting of research results by authors or by a combination of both. Typically, study results that are statistically significant or have larger estimated effect sizes are more likely to appear in the published literature, hence giving a biased picture of the evidence-base. Previous statistical approaches for addressing dissemination bias have assumed only a single selection mechanism. Here we consider a more realistic scenario in which multiple dissemination processes, involving both the publishing authors and journals, are operating. In practical applications, the methods can be used to provide sensitivity analyses for the potential effects of multiple dissemination biases operating in meta-analysis.

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Year:  2010        PMID: 20213702     DOI: 10.1002/sim.3813

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  7 in total

1.  A fully Bayesian application of the Copas selection model for publication bias extended to network meta-analysis.

Authors:  Dimitris Mavridis; Alex Sutton; Andrea Cipriani; Georgia Salanti
Journal:  Stat Med       Date:  2012-07-17       Impact factor: 2.373

2.  Adjustment for reporting bias in network meta-analysis of antidepressant trials.

Authors:  Ludovic Trinquart; Gilles Chatellier; Philippe Ravaud
Journal:  BMC Med Res Methodol       Date:  2012-09-27       Impact factor: 4.615

3.  Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression.

Authors:  Jack Bowden; George Davey Smith; Stephen Burgess
Journal:  Int J Epidemiol       Date:  2015-06-06       Impact factor: 7.196

4.  Weighing Evidence "Steampunk" Style via the Meta-Analyser.

Authors:  Jack Bowden; Chris Jackson
Journal:  Am Stat       Date:  2016-11-21       Impact factor: 8.710

5.  A sensitivity analysis framework for the treatment effect measure used in the meta-analysis of comparative binary data from randomised controlled trials.

Authors:  Dan Jackson; Rose Baker; Jack Bowden
Journal:  Stat Med       Date:  2012-09-02       Impact factor: 2.373

6.  Improving the error rates of the Begg and Mazumdar test for publication bias in fixed effects meta-analysis.

Authors:  Miriam Gjerdevik; Ivar Heuch
Journal:  BMC Med Res Methodol       Date:  2014-09-22       Impact factor: 4.615

Review 7.  Biases Inherent in Studies of Coffee Consumption in Early Pregnancy and the Risks of Subsequent Events.

Authors:  Alan Leviton
Journal:  Nutrients       Date:  2018-08-23       Impact factor: 5.717

  7 in total

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