Recently, two researchers from Queen Mary, University of London, conducted a peer-reviewed meta-analysis evaluating ivermectin (Bryant et al, 2021) and concluded that this antiparasitic drug is a cheap and effective treatment for reducing COVID-19 deaths. Importantly, these conclusions were in stark contrast to those of a later study (Roman et al, 2021). Although (Roman et al, 2021) applied the same classical statistical approach to meta-analysis, and produced similar results based on a subset of the same trials data used by (Bryant et al), they claimed there was insufficient quality of evidence to support the conclusion Ivermectin was effective. The Queen Mary, University of London investigators apply a Bayesian approach, to a subset of the same trial data, to test several causal hypotheses linking COVID-19 severity and ivermectin to mortality and produce an alternative analysis to the classical approach. Applying diverse alternative analysis methods, which reach the same conclusions, should increase overall confidence in the result. The authors demonstrate that there is overwhelming evidence to support a causal link between ivermectin, COVID-19 severity and mortality, and: i) for severe COVID-19, there is a 90.7% probability the risk ratio favors ivermectin; ii) for mild/moderate COVID-19, there is an 84.1% probability the risk ratio favors ivermectin. Also, from the Bayesian meta-analysis, for patients with severe COVID-19, the mean probability of death without ivermectin treatment is 22.9%, while with the application of ivermectin treatment, it is 11.7%. The paper also highlights the advantages of using Bayesian methods over classical statistical methods for meta-analysis.

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