Efficacy of New Generation Antidepressants: Differences Seem Illusory

Del Re, Aaron C.; Spielmans, Glen I.; Flückiger, Christoph; Wampold, Bruce E. (2013). Efficacy of New Generation Antidepressants: Differences Seem Illusory. PLoS ONE, 8(6), e63509. Public Library of Science 10.1371/journal.pone.0063509

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Background: Recently, Cipriani and colleagues examined the relative efficacy of 12 new-generation antidepressants on
major depression using network meta-analytic methods. They found that some of these medications outperformed others
in patient response to treatment. However, several methodological criticisms have been raised about network meta-analysis
and Cipriani’s analysis in particular which creates the concern that the stated superiority of some antidepressants relative to
others may be unwarranted.
Materials and Methods: A Monte Carlo simulation was conducted which involved replicating Cipriani’s network metaanalysis
under the null hypothesis (i.e., no true differences between antidepressants). The following simulation strategy was
implemented: (1) 1000 simulations were generated under the null hypothesis (i.e., under the assumption that there were no
differences among the 12 antidepressants), (2) each of the 1000 simulations were network meta-analyzed, and (3) the total
number of false positive results from the network meta-analyses were calculated.
Findings: Greater than 7 times out of 10, the network meta-analysis resulted in one or more comparisons that indicated the
superiority of at least one antidepressant when no such true differences among them existed.
Interpretation: Based on our simulation study, the results indicated that under identical conditions to those of the 117 RCTs
with 236 treatment arms contained in Cipriani et al.’s meta-analysis, one or more false claims about the relative efficacy of
antidepressants will be made over 70% of the time. As others have shown as well, there is little evidence in these trials that
any antidepressant is more effective than another. The tendency of network meta-analyses to generate false positive results
should be considered when conducting multiple comparison analyses.

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