Literature DB >> 17447944

Assessing the implications of publication bias for two popular estimates of between-study variance in meta-analysis.

Dan Jackson1.   

Abstract

Perhaps the greatest threat to the validity of a meta-analysis is the possibility of publication bias, where studies with interesting or statistically significant results are more likely to be published. This obviously impacts on inference concerning the treatment effect but also has implications for estimates of between-study variance. Two popular and established estimation methods are considered and formulae for assessing the implications of the bias are provided in terms of a general process for selecting studies. Meta-analysts, concerned that publication bias may be present, can use these as part of a sensitivity analysis to assess the robustness of their estimates of between-study variance using any selection process that is likely to be used in practice. The procedure is illustrated using a meta-analysis of clinical trials concerning the effectiveness of endoscopic sclerotherapy for preventing death in patients with cirrhosis and oesophagogastric varices.

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Year:  2007        PMID: 17447944     DOI: 10.1111/j.1541-0420.2006.00663.x

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  6 in total

1.  Maximum likelihood estimation and EM algorithm of Copas-like selection model for publication bias correction.

Authors:  Jing Ning; Yong Chen; Jin Piao
Journal:  Biostatistics       Date:  2017-07-01       Impact factor: 5.899

Review 2.  Heterogeneity of systematic reviews in oncology.

Authors:  Jonathan Holmes; David Herrmann; Chelsea Koller; Sarah Khan; Blake Umberham; Jody A Worley; Matt Vassar
Journal:  Proc (Bayl Univ Med Cent)       Date:  2017-04

3.  Rejoinder to "quantifying publication bias in meta-analysis".

Authors:  Lifeng Lin; Haitao Chu
Journal:  Biometrics       Date:  2017-11-15       Impact factor: 2.571

4.  Heterogeneity estimates in a biased world.

Authors:  Johannes Hönekopp; Audrey Helen Linden
Journal:  PLoS One       Date:  2022-02-03       Impact factor: 3.240

5.  A test for reporting bias in trial networks: simulation and case studies.

Authors:  Ludovic Trinquart; John P A Ioannidis; Gilles Chatellier; Philippe Ravaud
Journal:  BMC Med Res Methodol       Date:  2014-09-27       Impact factor: 4.615

6.  Evolution of heterogeneity (I2) estimates and their 95% confidence intervals in large meta-analyses.

Authors:  Kristian Thorlund; Georgina Imberger; Bradley C Johnston; Michael Walsh; Tahany Awad; Lehana Thabane; Christian Gluud; P J Devereaux; Jørn Wetterslev
Journal:  PLoS One       Date:  2012-07-25       Impact factor: 3.240

  6 in total

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