Visualizing the Evolution of Evidence: Cumulative Network Meta-Analyses of New Generation Antidepressants in the Last 40 Years.

Luo, Yan; Chaimani, Anna; Furukawa, Toshi A; Kataoka, Yuki; Ogawa, Yusuke; Cipriani, Andrea; Salanti, Georgia (2021). Visualizing the Evolution of Evidence: Cumulative Network Meta-Analyses of New Generation Antidepressants in the Last 40 Years. Research Synthesis Methods, 12(1), pp. 74-85. Wiley 10.1002/jrsm.1413

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It is often challenging to present the available evidence in a timely and comprehensible manner. We aimed to visualize the evolution of evidence about antidepressants for depression by conducting cumulative network meta-analyses (NMAs) and to examine whether it could have helped the selection of optimal drugs. We built a Shiny web application that performs and presents cumulative NMAs based on R netmeta. We used a comprehensive dataset of double-blind randomized controlled trials of 21 antidepressants in the acute treatment of major depression. The primary outcomes were efficacy (treatment response) and acceptability (all-cause discontinuation), and treatment effects were summarized via odds ratios. We evaluated the confidence in evidence using the CINeMA (Confidence in Network Meta-Analysis) framework for a series of consecutive NMAs. Users can change several conditions for the analysis, such as the period of synthesis, among the others. We present the league tables and two-dimensional plots that combine efficacy, acceptability and level of confidence in the evidence together, for NMAs conducted in 1990, 1995, 2000, 2005, 2010 and 2016. They reveal that through the past four decades, newly approved drugs often showed initially exaggerated results, which tended to diminish and stabilize after approximately a decade. Over the years, the drugs with relative superiority changed dramatically; but as the evidence network grew larger and better connected, the overall confidence improved. The Shiny app visualizes how evidence evolved over years, emphasizing the need for a careful interpretation of relative effects between drugs, especially for the potentially amplified performance of newly approved drugs. This article is protected by copyright. All rights reserved.

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