Literature DB >> 12464378

Generalized synthesis of evidence and the threat of dissemination bias. the example of electronic fetal heart rate monitoring (EFM).

Alexander J Sutton1, Keith R Abrams, David R Jones.   

Abstract

Assessment of the potential impact of dissemination bias is necessary for meta-analysis. When evidence is available from studies of different designs, the different study types may be affected by dissemination bias to differing degrees. The evidence relating to electronic fetal heart rate monitoring (EFM) for preventing perinatal mortality is used to explore the feasibility of carrying out a dissemination bias assessment in a generalized synthesis of evidence (gse) framework. Visual inspection of funnel plots, statistical tests, and methods to "adjust" the results of a meta-analysis are all used in an extensive sensitivity analysis. The potential impact of dissemination bias on gse models synthesizing all the evidence together is also reported. Detailed consideration is given to the influence of meta-analysis model choice, and outcome scale used. Using the risk difference scale, funnel plots of the observational studies appeared highly asymmetric. However, further explorations show these conclusions are not robust over use of different outcome measures or different meta-analysis models. Researchers should be aware that dissemination bias may affect different sources of evidence differently. Although assessments such as those described here are recommended, awareness of their lack of robustness to outcome scale and model choice is important. Further research into methods to assess dissemination bias that are invariant to these factors is needed.

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Year:  2002        PMID: 12464378     DOI: 10.1016/s0895-4356(02)00460-2

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


  4 in total

Review 1.  Reasons or excuses for avoiding meta-analysis in forest plots.

Authors:  John P A Ioannidis; Nikolaos A Patsopoulos; Hannah R Rothstein
Journal:  BMJ       Date:  2008-06-21

Review 2.  Computational meta-analysis of statistical parametric maps in major depression.

Authors:  Danilo Arnone; Dominic Job; Sudhakar Selvaraj; Osamu Abe; Francesco Amico; Yuqi Cheng; Sean J Colloby; John T O'Brien; Thomas Frodl; Ian H Gotlib; Byung-Joo Ham; M Justin Kim; P Cédric M P Koolschijn; Cintia A-M Périco; Giacomo Salvadore; Alan J Thomas; Marie-José Van Tol; Nic J A van der Wee; Dick J Veltman; Gerd Wagner; Andrew M McIntosh
Journal:  Hum Brain Mapp       Date:  2016-02-08       Impact factor: 5.038

3.  Methods to systematically review and meta-analyse observational studies: a systematic scoping review of recommendations.

Authors:  Monika Mueller; Maddalena D'Addario; Matthias Egger; Myriam Cevallos; Olaf Dekkers; Catrina Mugglin; Pippa Scott
Journal:  BMC Med Res Methodol       Date:  2018-05-21       Impact factor: 4.615

Review 4.  Bayesian methods for evidence synthesis in cost-effectiveness analysis.

Authors:  A E Ades; Mark Sculpher; Alex Sutton; Keith Abrams; Nicola Cooper; Nicky Welton; Guobing Lu
Journal:  Pharmacoeconomics       Date:  2006       Impact factor: 4.981

  4 in total

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