Efficacy of drug treatments for generalised anxiety disorder: systematic review and meta-analysis

We have already raised the issue about the inclusion in this network meta-analysis of the 3-arm study by Silverstone and colleagues, which compared fluoxetine, venlafaxine and placebo (Silvestone 2001). See http://www.bmj.com/content/342/bmj.d1199/rapid-responses. We further add some comments on the methodology employed.

In the network meta-analysis (Baldwin 2011) it is reported that the authors “tested the validity of the mixed treatment model by comparing the consistency of results between the mixed treatment meta-analyses and the direct comparison meta-analyses.” It has been previously suggested that this is not a valid method for assessing inconsistency because the network estimates (“mixed treatment meta-analyses”) are a combination of the direct (“direct comparison meta-analyses”) and indirect estimates and consequently they are not expected to differ much, even in the presence of substantial inconsistency (Nikolakopoulou 2014). As network meta-analysis technique is quickly gaining popularity in the literature (but quality is still variable – Nikolakopoulou 2014), we re-analyzed the 27 studies included in this network following the methods reported in the original article and compared our findings with the published results. We summarise here some of the main results we found and the full report with all our comments will be submitted soon to a peer-reviewed scientific journal.

In our re-analysis, both the design-by-treatment interaction test (Higgins 2012) and the loop-specific approach (Salanti 2009; Bucher 1997; Chaimani 2013) did not reveal statistically significant inconsistency, however the large uncertainty in the estimation of the ratio of odds ratios (ROR) between direct and indirect estimates in 3 out of 6 loops would have deserved further exploration as they can’t exclude the possibility of substantial inconsistency. It is probably not by chance that the loop including Silvestone 2001 study (fluoxetine, placebo, venlafaxine) had the largest upper confidence interval (CI) limit (ROR=2.51, 95% CI 1.00 to 11.43) and this reinforced our concerns about the inclusion of this study in the network (and therefore, the validity of the overall results from the published paper).

According to our re-analysis, in terms of response, all drugs - except fluoxetine, quetiapine and paroxetine - appeared to be statistically significantly more effective than placebo. However, no important differences existed between the ten interventions in terms of efficacy. So, it is very likely that the advantage of fluoxetine in the hierarchy of the published report was the consequence of using an inappropriate ranking measure that did not properly account for statistical uncertainty. In fact, we ranked efficacy of treatments using the surface under the cumulative ranking curve percentages (the so-called SUCRA - Salanti 2011, Chaimani 2013) and the mean ranks, and we found different results: lorazepam ranked first in terms of response (75.8%) and escitalopram for remission (80.8%) (Table 1). Fluoxetine was still among the drugs with potentially better efficacy profile, however the small differences among treatments suggest that the most sensible and appropriate conclusion from Baldwin 2011 data would have been that there is too large uncertainty around the hierarchy of treatments for generalized anxiety disorder.

Table 1. SUCRA values for response, withdrawals and remission.

Treatment Response Withdrawals Remission
Placebo 5.0% 91.9% 1.2%
Escitalopram 53.3% 51.0% 80.8%
Tiagabine 22.9% 39.4% 17.4%
Duloxetine 61.1% 18.3% 39.0%
Paroxetine 51.2% 30.7% 56.6%
Pregabalin 52.3% 65.5% -
Venlafaxine 60.1% 32.9% 64.8%
Lorazepam 75.8% 16.6% -
Fluoxetine 67.6% 71.4% 80.3%
Sertraline 60.3% 82.4% 60.0%
Quetiapine 40.5% 91.9% -

We hope that these comments will help researchers and clinicians recognize the complexity of conducting a high-quality network meta-analysis, which require a multi-disciplinary team with clinical and technical expertise to adequately cover each step of the research project, including skills in literature search, data extraction, and statistical analysis.

As for all systematic review and standard meta-analyses, results from network meta-analyses should be replicable. Published papers must include all of the information that readers need to completely understand how the study was conducted, independently assess the validity of the analyses and reach their own interpretations. The availability of the review protocol and the codes for statistical analyses should become soon a mandatory requirement for all network meta-analyses.

Finally, we would like to thank Gillian Sibbring and Complete Clarity for providing us with the original dataset and some of the codes they used for the analyses of Baldwin 2011 paper.

Andrea Cipriani,1 Anna Chaimani,2 Georgia Salanti,2 Stefan Leucht,3 John R. Geddes,1

1 Department of Psychiatry, University of Oxford, UK; 2 Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece; 3 Department of Psychiatry and Psychotherapy TU-München, Munich, Germany;

References

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