Effects of antidepressant medicines on preventing relapse of unipolar depression: a pooled analysis of parametric survival curves

Psychol Med. 2022 Jan;52(1):48-56. doi: 10.1017/S0033291720001610. Epub 2020 Jun 5.

Abstract

Background: Major depressive disorder is characterized by a high risk of relapse. We aimed to compare the prophylactic effects of different antidepressant medicines (ADMs).

Methods: PubMed, Cochrane Central Register of Controlled Trials, Embase and the Web of Science were searched on 4 July 2019. A pooled analysis of parametric survival curves was performed using a Bayesian framework. The main outcomes were hazard ratios (HRs), relapse-free survival and mean relapse-free months.

Results: Forty randomized controlled trials were included. The 1-year relapse-free survival for ADM (76%) was significantly better than that for placebo (56%). Most of the relapse difference (86.5%) occurred in the first 6 months. Most HRs were not constant over time. Proof of benefit after 6 months of follow-up was not established partially because of small differences between the drug and placebo after 6 months. Almost all studies used an 'enriched' randomized discontinuation design, which may explain the high relapse rates in the first 6 months after randomization.

Conclusions: The superiority of ADM v. placebo was mainly attributed to the difference in relapse rates that occurred in the first 6 months. Our analysis provided evidence that the prophylactic efficacy was not constant over time. A beneficial effect was observed, but the prevention of new episodes after 6 months was questionable. These findings may have implications for clinical practice.

Keywords: Antidepressants; major depressive disorder; recurrence; relapse.

Publication types

  • Meta-Analysis

MeSH terms

  • Antidepressive Agents / therapeutic use
  • Bayes Theorem
  • Chronic Disease
  • Depressive Disorder, Major* / drug therapy
  • Depressive Disorder, Major* / prevention & control
  • Humans
  • Recurrence

Substances

  • Antidepressive Agents