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Novel methods to deal with publication biases: secondary analysis of antidepressant trials in the FDA trial registry database and related journal publications

Research output: Contribution to journalJournal articlepeer-review

Published
  • Santiago G. Moreno
  • Alex J. Sutton
  • Erick H. Turner
  • Keith R. Abrams
  • Nicola J. Cooper
  • Tom M. Palmer
  • A. E. Ades
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Article numberb2981
<mark>Journal publication date</mark>2009
<mark>Journal</mark>BMJ
Volume339
Number of pages7
Publication StatusPublished
Early online date7/08/09
<mark>Original language</mark>English

Abstract

OBJECTIVE: To assess the performance of novel contour enhanced funnel plots and a regression based adjustment method to detect and adjust for publication biases.

DESIGN: Secondary analysis of a published systematic literature review.

DATA SOURCES: Placebo controlled trials of antidepressants previously submitted to the US Food and Drug Administration (FDA) and matching journal publications.

METHODS: Publication biases were identified using novel contour enhanced funnel plots, a regression based adjustment method, Egger's test, and the trim and fill method. Results were compared with a meta-analysis of the gold standard data submitted to the FDA.

RESULTS: Severe asymmetry was observed in the contour enhanced funnel plot that appeared to be heavily influenced by the statistical significance of results, suggesting publication biases as the cause of the asymmetry. Applying the regression based adjustment method to the journal data produced a similar pooled effect to that observed by a meta-analysis of the FDA data. Contrasting journal and FDA results suggested that, in addition to other deviations from study protocol, switching from an intention to treat analysis to a per protocol one would contribute to the observed discrepancies between the journal and FDA results.

CONCLUSION: Novel contour enhanced funnel plots and a regression based adjustment method worked convincingly and might have an important part to play in combating publication biases.