Literature DB >> 19282148

Empirical evaluation showed that the Copas selection model provided a useful summary in 80% of meta-analyses.

James R Carpenter1, Guido Schwarzer, Gerta Rücker, Rita Künstler.   

Abstract

OBJECTIVE: Although using meta-analysis to combine evidence from a number of studies should reduce both bias and uncertainty, it is sometimes not the case, because published studies represent a biased selection of the evidence. Copas proposed a selection model to assess the sensitivity of meta-analysis conclusions to possible selection bias. However, this relatively complex model awaits both reliable software and an empirical evaluation. This article reports work addressing both these issues. STUDY DESIGN AND
SETTING: We took 157 meta-analyses with binary outcomes, analyzed each one using the Copas selection model, and evaluated each analysis using a prespecified protocol. The evaluation aimed to assess the usefulness of the Copas selection model to a typical Cochrane reviewer.
RESULTS: In approximately 80% of meta-analyses, the overall interpretation of the Copas selection model was clear, with better results among the 22 with evidence of selection bias. However, as with the "Trim and Fill" method, allowing for selection bias can result in smaller standard errors for the treatment estimate.
CONCLUSION: When a reliable test for selection bias is significant, we recommend systematic reviewers to try the Copas selection model, although the results should be interpreted cautiously.

Mesh:

Year:  2009        PMID: 19282148     DOI: 10.1016/j.jclinepi.2008.12.002

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  8 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

2.  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

Review 3.  Association Between Initial Use of e-Cigarettes and Subsequent Cigarette Smoking Among Adolescents and Young Adults: A Systematic Review and Meta-analysis.

Authors:  Samir Soneji; Jessica L Barrington-Trimis; Thomas A Wills; Adam M Leventhal; Jennifer B Unger; Laura A Gibson; JaeWon Yang; Brian A Primack; Judy A Andrews; Richard A Miech; Tory R Spindle; Danielle M Dick; Thomas Eissenberg; Robert C Hornik; Rui Dang; James D Sargent
Journal:  JAMA Pediatr       Date:  2017-08-01       Impact factor: 16.193

4.  Assessing treatment effects and publication bias across different specialties in medicine: a meta-epidemiological study.

Authors:  Simon Schwab; Giuachin Kreiliger; Leonhard Held
Journal:  BMJ Open       Date:  2021-09-14       Impact factor: 3.006

5.  The effect of publication bias magnitude and direction on the certainty in evidence.

Authors:  Mohammad Hassan Murad; Haitao Chu; Lifeng Lin; Zhen Wang
Journal:  BMJ Evid Based Med       Date:  2018-04-12

6.  Serious adverse events following treatment of visceral leishmaniasis: A systematic review and meta-analysis.

Authors:  Sauman Singh-Phulgenda; Prabin Dahal; Roland Ngu; Brittany J Maguire; Alice Hawryszkiewycz; Sumayyah Rashan; Matthew Brack; Christine M Halleux; Fabiana Alves; Kasia Stepniewska; Piero L Olliaro; Philippe J Guerin
Journal:  PLoS Negl Trop Dis       Date:  2021-03-29

7.  Simpson's paradox visualized: the example of the rosiglitazone meta-analysis.

Authors:  Gerta Rücker; Martin Schumacher
Journal:  BMC Med Res Methodol       Date:  2008-05-30       Impact factor: 4.615

8.  Meta-analysis and The Cochrane Collaboration: 20 years of the Cochrane Statistical Methods Group.

Authors:  Joanne E McKenzie; Georgia Salanti; Steff C Lewis; Douglas G Altman
Journal:  Syst Rev       Date:  2013-11-26
  8 in total

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