The use of the meta-regression method to indirectly compare duloxetine with each active comparator revealed that there was no significant difference with fluoxetine either in efficacy or in safety. Findings only suggest that more patients might respond to duloxetine. Results suggest that duloxetine might be significantly less effective compared with venlafaxine, (in terms of treatment effects and number of response) with similar dropouts rates.
Results given by sensitivity analyses showed relatively good consistency, as no analysis changed the conclusions. The results became nonsignificant in one analysis comparing venlafaxine with duloxetine, but the estimated value seldom moved. When removing [4
] or [5
] from the analysis set, duloxetine treated patients had statistically more chance to respond than when treated with fluoxetine. These findings were obtained by removing the less favourable studies for duloxetine, and we found no differences in the design or patients' characteristics that may explain why. These tests showing significance (when comparing fluoxetine to duloxetine) or non-significance (when comparing venlafaxine to duloxetine), as in every study where multiple testing is performed, may be due to a drop in statistical power, which can bias the conclusions. As some robust trends have been found between the different drugs, the findings are considered robust to the confounding factors that have been investigated.
Our findings should, however, be interpreted with caution. Findings of superior efficacy by indirect comparisons are observational and therefore vulnerable to bias. Yet, several articles have recently shown that indirect comparisons adjusted at the aggregate level usually agree with direct comparisons. An indirect meta-analysis of studies comparing olanzapine with haloperidol and risperidone with haloperidol yielded conclusions similar to those found in a direct comparative randomized clinical trial of olanzapine and risperidone [43
]. Song et al
] demonstrated that the results of adjusted indirect comparisons were usually similar to those of direct comparisons. In their study, there were a few significant discrepancies between the direct and the indirect estimates, although the direction of discrepancy was unpredictable. The authors concluded that empirical evidence presented in their study clearly indicates that in most cases, results of adjusted indirect comparisons are not significantly different from those of direct comparisons.
While we recognize that none of the trials involving duloxetine used venlafaxine as an active comparator, our results are in accordance with a recent meta-analysis comparing duloxetine and venlafaxine in the treatment of MDD [45
] and a review comparing second-generation antidepressants [46
Vis et al. used results of 6 trials with duloxetine and 4 with venlafaxine to report the efficacy and safety of either venlafaxine or duloxetine compared with placebo. They found that venlafaxine rates for remission and response were respectively 17.8% (CI95% 9.0–26.5) and 24.4% (CI95% 15.0–37.7) greater than placebo, compared with 14.2% (CI95% 8.9–26.5) and 18.6% (CI95% 13.0–24.2) for duloxetine. Reported adverse events were comparable between active drugs. The authors concluded that venlafaxine showed a favorable trend in remission and response rates compared with duloxetine, but that no significant between-drug differences were observed for dropout rates and adverse events. Due to the nature of the methodology used, no objective evidence concerning how venlafaxine performs when compared with duloxetine can be drawn. Nonetheless, the numerical trend seen in this paper is in accordance with the ones found here.
A review of second-generation antidepressants' efficacy in the treatment of MDD by Hansen et al
] found that significantly more patients responded to venlafaxine than to fluoxetine. The relative benefit: 1.12 (CI95%
1.02–1.23) favoured venlafaxine. This result suggest the same pattern found here; response rates of venlafaxine are superior to duloxetine which are equal to fluoxetine
Concerning available comparisons with fluoxetine, of the 9 randomized clinical trials that evaluated the efficacy and safety of duloxetine, only two used fluoxetine as an active comparator [4
]. Neither of these studies was specifically designed and powered to facilitate head-to-head comparisons between duloxetine and fluoxetine. The primary goal was comparison of duloxetine vs
. placebo. These two studies (powered 65%) were identical parallel group, double-blind, forced-titration active- and placebo-controlled studies comparing duloxetine titrated from 20 mg to 60 mg BID with placebo over 8 weeks of acute treatment. A fluoxetine 20 mg QD arm was used as an internal active comparator standard. In these studies, duloxetine was statistically significantly superior to placebo on the primary analysis (mean change analysis from baseline of the HAMD-17 total score) and for some of the secondary endpoints. There was no statistically significant difference between fluoxetine and placebo for mean change in HAMD-17 total score in any of the studies. The fluoxetine treatments groups were underpowered qualitative control arms: [1
] half patients included compared with duloxetine and placebo reaching low numbers (33 [9
] and 37 [4
] comparison of a fixed dose at the minimum recommended range for fluoxetine (20 mg/day) with the highest tested dose for duloxetine (120 mg/day). Higher doses of fluoxetine may have proven more effective and a more robust comparison of duloxetine, and fluoxetine should include a broader and more optimal dose range for comparison. Furthermore, as fluoxetine has proven to have an effect when compared with placebo [47
], these direct comparisons are not sufficient to draw conclusions about duloxetine's superiority over fluoxetine.
Superiority of one antidepressant medication relative to another needs to be established by means of prospectively designed, adequately powered, head-to-head clinical trials. As the results of placebo-controlled trials are often sufficient to acquire the regulatory approval of new drugs, pharmaceutical companies may not be motivated to support trials that compare new drugs with existing active treatments. Lack of evidence from direct comparison between active interventions makes it difficult for clinicians to choose the most effective treatment for patients [49
]. Because of the lack of direct evidence, indirect comparisons have been recommended [50
]. Adjusted indirect comparison is a way to compare two compounds through their relative effect vs
. a common comparator (placebo in our study). The indirect approach to meta-analysis requires certain conditions to yield optimal results. Differences in study designs, inclusion/exclusion criteria, patients characteristics at baseline as well as difference in drug dosage [48
] and publication bias are limitations that may lead to unbalanced conclusions [43
] and merit discussion.
Our study had some limitations. First, the time frame differs between active drugs. Because fluoxetine is the oldest antidepressant compared with venlafaxine and duloxetine, inclusion criteria for MDD was based on DSM III or IIIr criteria (not DSM IV) in the majority of the fluoxetine studies compared with those of venlafaxine and duloxetine. Secondly, sample sizes seem to be smaller for the fluoxetine studies and include patients with lower HAM-D score (14 to 19). Thirdly the patients characteristics, even if they vary only slightly can act as confounding factors and bias the results. Fourthly, dosages varied between studies and between drugs. Lastly, the missing data might not be balanced between treatments. All these sources of heterogeneity could lead to bias. Considering that the computation of an effect size included adjustment for baseline severity differences and that influence of patient characteristics and study designs were assessed through sensitivity analyses, some confidence can be put on the results if they show stability over the different analyses. Also, the random effect nature of the model used here should be able to deal with the remaining amount of bias that couldn't be measured or properly modelled. Finally, the other major issue in any meta-analysis is the potential publication bias. Publication bias is a major source of systematic bias in overviews, where trials with positive results are more likely to be published than those with neutral or negative results, especially if the trials are small. We therefore tested for publication bias using the Egger test for funnel plot asymmetry [51
]. Ruling out completely publication bias is nearly impossible. Even so, any bias would most likely be in favour of the newer drug and its existence would not undermine the results presented here [52