Are antidepressants clinically useful? Conclusion of a decade of debate

Use of Quantile Treatment Effects Analysis to Describe Antidepressant Response in Randomized Clinical Trials Submitted to the US Food and Drug Administration: A Secondary Analysis of Pooled Trial Data

Importance

Major depressive disorder (MDD) is a leading cause of global distress and disability. Earlier studies have indicated that antidepressant therapy confers a modest reduction in depressive symptoms on average, but the distribution of this reduction requires more research.

Objective

To estimate the distribution of antidepressant response by depression severity.

Design, Setting, and Participants

In this secondary analysis of pooled trial data, quantile treatment effect (QTE) analysis was conducted from the US Food and Drug Administration (FDA) database of antidepressant monotherapy for patients with MDD, encompassing 232 positive and negative trials submitted to the FDA between 1979 and 2016. Analysis was restricted to participants with severe MDD (17-item Hamilton Rating Scale for Depression [HAMD-17] score ≥20). Data analysis was conducted from August 16, 2022, to April 16, 2023.

Intervention

Antidepressant monotherapy compared with placebo.

Main Outcomes and Measures

The distribution of percentage depression response was compared between the pooled treatment arm and pooled placebo arm. Percentage depression response was defined as 1 minus the ratio of final depression severity to baseline depression severity, expressed as a percentage. Depression severity was reported in HAMD-17-equivalent units.

Results

A total of 57 313 participants with severe depression were included in the analysis. There was no significant imbalance in baseline depression severity between the pooled treatment arm and pooled placebo arm, with a mean HAMD-17 difference of 0.037 points (P = .11 by Wilcoxon rank sum test). An interaction term test for rank similarity did not reject the rank similarity governing percentage depression response (P > .99). The entire distribution of depression response was more favorable in the pooled treatment arm than in the pooled placebo arm. The maximum separation between treatment and placebo occurred at the 55th quantile and corresponded to an absolute improvement in depression due to active drug of 13.5% (95% CI, 12.4%-14.4%). The separation between treatment and placebo diminished near the tails of the distribution.

Conclusions and Relevance

In this QTE analysis of pooled clinical trial data from the FDA, antidepressants were found to confer a small reduction in depression severity that was broadly distributed across participants with severe depression. Alternatively, if the assumptions behind the QTE analysis are not met, then the data are also compatible with antidepressants eliciting more complete response in a smaller subset of participants than is suggested by this QTE analysis.

Determining maximal achievable effect sizes of antidepressant therapies in placebo-controlled trials

OBJECTIVE

Antidepressants outperform placebo with an effect size of around 0.30. It has been suggested that effect sizes as high as 0.875 are necessary for a minimal clinically important difference. Whether such effect sizes are achievable in placebo-controlled trials is unknown. Therefore, we aimed to assess what effect sizes are theoretically achievable in placebo-controlled trials of antidepressants.

METHODS

Patient-level analyses comparing Hamilton Depression Rating Scale (HDRS-17) outcomes for simulated antidepressant therapies to placebo-treated participants (n = 2201) from clinical trials of selective serotonin reuptake inhibitors.

RESULTS

An optimally effective antidepressant, where all treated participants achieve HDRS-17 scores comparable to those displayed by healthy volunteers (remission-type model), had a maximum effect size of 1.75, with a mean difference of 11.6 points on the HDRS-17. In simulations where patients received an additional 50% symptom reduction over that obtained with placebo (improvement-type model), the maximum effect size was 1.08 with a mean HDRS-17 difference of 7.2. When adjusting for normal rates of treatment discontinuation, maximum effect sizes were 1.10 (remission-type model) and 0.76 (improvement-type model) with HDRS-17 mean differences of 8.8 and 5.6, respectively.

CONCLUSIONS

Three methodological issues (i) a large and variable placebo response, (ii) a high rate of dropout and (iii) HDRS-17-ratings significantly larger than zero in healthy volunteers, reduce the degree of treatment-placebo separation achievable in depression trials. Assuming that those who discontinue treatment have only partial response, even a highly effective antidepressant would have difficulties surpassing such effect size cut-offs as have been suggested to signify a minimal clinically important difference.

EPA guidance on improving the image of psychiatry

This paper explores causes, explanations and consequences of the negative image of psychiatry and develops recommendations for improvement. It is primarily based on a WPA guidance paper on how to combat the stigmatization of psychiatry and psychiatrists and a Medline search on related publications since 2010. Furthermore, focussing on potential causes and explanations, the authors performed a selective literature search regarding additional image-related issues such as mental health literacy and diagnostic and treatment issues. Underestimation of psychiatry results from both unjustified prejudices of the general public, mass media and healthcare professionals and psychiatry's own unfavourable coping with external and internal concerns. Issues related to unjustified devaluation of psychiatry include overestimation of coercion, associative stigma, lack of public knowledge, need to simplify complex mental issues, problem of the continuum between normality and psychopathology, competition with medical and non-medical disciplines and psychopharmacological treatment. Issues related to psychiatry's own contribution to being underestimated include lack of a clear professional identity, lack of biomarkers supporting clinical diagnoses, limited consensus about best treatment options, lack of collaboration with other medical disciplines and low recruitment rates among medical students. Recommendations are proposed for creating and representing a positive self-concept with different components. The negative image of psychiatry is not only due to unfavourable communication with the media, but is basically a problem of self-conceptualization. Much can be improved. However, psychiatry will remain a profession with an exceptional position among the medical disciplines, which should be seen as its specific strength.