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Methods for detecting, quantifying, and adjusting for dissemination bias in meta-analysis are described


Mueller, Katharina Felicitas; Meerpohl, Joerg J; Briel, Matthias; Antes, Gerd; von Elm, Erik; Lang, Britta; Motschall, Edith; Schwarzer, Guido; Bassler, Dirk (2016). Methods for detecting, quantifying, and adjusting for dissemination bias in meta-analysis are described. Journal of Clinical Epidemiology, 80:25-33.

Abstract

OBJECTIVE To systematically review methodological articles which focus on nonpublication of studies and to describe methods of detecting and/or quantifying and/or adjusting for dissemination in meta-analyses. To evaluate whether the methods have been applied to an empirical data set for which one can be reasonably confident that all studies conducted have been included. STUDY DESIGN AND SETTING We systematically searched Medline, the Cochrane Library, and Web of Science, for methodological articles that describe at least one method of detecting and/or quantifying and/or adjusting for dissemination bias in meta-analyses. RESULTS The literature search retrieved 2,224 records, of which we finally included 150 full-text articles. A great variety of methods to detect, quantify, or adjust for dissemination bias were described. Methods included graphical methods mainly based on funnel plot approaches, statistical methods, such as regression tests, selection models, sensitivity analyses, and a great number of more recent statistical approaches. Only few methods have been validated in empirical evaluations using unpublished studies obtained from regulators (Food and Drug Administration, European Medicines Agency). CONCLUSION We present an overview of existing methods to detect, quantify, or adjust for dissemination bias. It remains difficult to advise which method should be used as they are all limited and their validity has rarely been assessed. Therefore, a thorough literature search remains crucial in systematic reviews, and further steps to increase the availability of all research results need to be taken.

Abstract

OBJECTIVE To systematically review methodological articles which focus on nonpublication of studies and to describe methods of detecting and/or quantifying and/or adjusting for dissemination in meta-analyses. To evaluate whether the methods have been applied to an empirical data set for which one can be reasonably confident that all studies conducted have been included. STUDY DESIGN AND SETTING We systematically searched Medline, the Cochrane Library, and Web of Science, for methodological articles that describe at least one method of detecting and/or quantifying and/or adjusting for dissemination bias in meta-analyses. RESULTS The literature search retrieved 2,224 records, of which we finally included 150 full-text articles. A great variety of methods to detect, quantify, or adjust for dissemination bias were described. Methods included graphical methods mainly based on funnel plot approaches, statistical methods, such as regression tests, selection models, sensitivity analyses, and a great number of more recent statistical approaches. Only few methods have been validated in empirical evaluations using unpublished studies obtained from regulators (Food and Drug Administration, European Medicines Agency). CONCLUSION We present an overview of existing methods to detect, quantify, or adjust for dissemination bias. It remains difficult to advise which method should be used as they are all limited and their validity has rarely been assessed. Therefore, a thorough literature search remains crucial in systematic reviews, and further steps to increase the availability of all research results need to be taken.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Neonatology
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:5 August 2016
Deposited On:09 Sep 2016 14:23
Last Modified:13 Dec 2016 02:01
Publisher:Elsevier
ISSN:0895-4356
Publisher DOI:https://doi.org/10.1016/j.jclinepi.2016.04.015
PubMed ID:27502970

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