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Comment
. 2023 Feb;86(2):154-225.
doi: 10.1016/j.jinf.2022.11.016. Epub 2022 Nov 25.

There is no reasonable evidence to support efficacy of fluvoxamine in prevention of disease deterioration in COVID-19 outpatients: A comment on two recent meta-analyses advocating its use

Vladimir Trkulja  1
Affiliations
Comment

There is no reasonable evidence to support efficacy of fluvoxamine in prevention of disease deterioration in COVID-19 outpatients: A comment on two recent meta-analyses advocating its use

Vladimir Trkulja. J Infect. 2023 Feb.
No abstract available

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Conflict of interest statement

Declaration of Competing Interest I have no financial or non-financial conflict of interest to declare.

Figures

Fig. 1
Fig. 1
Meta-analysis of randomized placebo-controlled trials of fluvoxamine outlined in Table 1. Please note, “Bramante 2022 1″ refers to a comparison of fluvoxamine + metformin placebo vs. “double” matching placebo, while “Bramante 2022 2″ refers to the comparison of fluvoxamine + metformin vs. fluvoxamine placebo + metformin. Since both comparisons come from the same trial, usually the “overall” estimate of fluvoxamine vs. placebo is referred to. However, as shown here, the two comparisons yielded estimates in opposing directions (albeit, imprecise): by disregarding this discrepancy (as small as it might be) and using the “raw overall estimate”, one artificially reduces heterogeneity across fluvoxamine vs. placebo comparisons. A Frequentist random effects meta-analysis/meta-regression addressed “study size” as a categorical moderator and yielded effect and heterogeneity estimates at each level of the moderator, as well as the overall one (restricted maximum likelihood estimator of τ2, with Hartung-Knapp-Sidik-Jonkman adjustment to t-distribution). B Bayesian random-effects meta-analysis/meta-regression used the same approach, but under the Bayesian framework, with weakly informative prior for τ2 (half Cahuchy, scale=0.5), and moderately informative skeptical prior for the pooled estimate compatible with the a priori hypothesis of no treatment effect [normal (0.0, 0.355) for ln(RR) – assigns 50% probability to RR <1.0, and 50% probability to RR >1.0].
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References

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