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J Clin Epidemiol. 2016 Dec;80:25-33. doi: 10.1016/j.jclinepi.2016.04.015. Epub 2016 Aug 5.

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

Author information

  • 1Center for Pediatric Clinical Studies, University Children's Hospital Tuebingen, Frondsbergstraße 23, 72070 Tuebingen, Germany.
  • 2Cochrane Germany, Medical Center-University of Freiburg, Berliner Allee 29, 79110 Freiburg im Breisgau, Germany.
  • 3Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, Hebelstrasse 10, 4031 Basel, Switzerland.
  • 4Cochrane Switzerland, IUMSP, University Hospital Lausanne, Route de la Corniche 10, 1010 Lausanne, Switzerland.
  • 5Center for Medical Biometry and Medical Informatics, Medical Center-University of Freiburg, Freiburg, Germany.
  • 6Department of Neonatology, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091 Zurich, Switzerland. Electronic address: dirk.bassler@usz.ch.

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 Only few methods have been validated in empirical evaluations using unpublished studies obtained from regulators (Food and Drug Administration, European Medicines Agency).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.

KEYWORDS:

Dissemination bias; Full publication; Publication bias; Small-study effect; Statistical methods; The OPEN project; Underreporting

PMID:
27502970
DOI:
10.1016/j.jclinepi.2016.04.015
[PubMed - in process]

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