Toward evidence-based medical statistics: a Bayesian analysis of double-blind placebo-controlled antidepressant trials in the treatment of anxiety disorders

Rei Monden*, Stijn de Vos, Richard Morey, Eric-Jan Wagenmakers, Peter de Jonge, Annelieke M. Roest

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)


The Food and Drug Administration (FDA) uses a p < 0.05 null-hypothesis significance testing framework to evaluate "substantial evidence" for drug efficacy. This framework only allows dichotomous conclusions and does not quantify the strength of evidence supporting efficacy. The efficacy of FDA-approved antidepressants for the treatment of anxiety disorders was re-evaluated in a Bayesian framework that quantifies the strength of the evidence. Data from 58 double-blind placebo-controlled trials were retrieved from the FDA for the second-generation antidepressants for the treatment of anxiety disorders. Bayes factors (BFs) were calculated for all treatment arms compared to placebo and were compared with the corresponding p-values and the FDA conclusion categories. BFs ranged from 0.07 to 131,400, indicating a range of no support of evidence to strong evidence for the efficacy. Results also indicate a varying strength of evidence between the trials with p < 0.05. In sum, there were large differences in BFs across trials. Among trials providing "substantial evidence" according to the FDA, only 27 out of 59 dose groups obtained strong support for efficacy according to the typically used cutoff of BF ≥ 20. The Bayesian framework can provide valuable information on the strength of the evidence for drug efficacy. Copyright © 2016 John Wiley & Sons, Ltd.

Original languageEnglish
Pages (from-to)299-308
Number of pages10
JournalInternational Journal of Methods in Psychiatric Research
Issue number4
Publication statusPublished - Dec-2016


  • second-generation antidepressants
  • FDA
  • Bayesian statistics
  • Bayes factor
  • significance testing

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