  Ivermectin is an antiparasitic drug that some have claimed is an effective
treatment for reducing Covid-19 deaths. To test this claim, two recent peer
reviewed papers both conducted a meta-analysis on a similar set of randomized
controlled trials data, applying the same classical statistical approach.
Although the statistical results were similar, one of the papers (Bryant et al,
2021) concluded that ivermectin was effective for reducing Covid-19 deaths,
while the other (Roman et al, 2021) concluded that there was insufficient
quality of evidence to support the conclusion Ivermectin was effective. This
paper applies 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. We show that there is strong 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 favours
ivermectin; ii) for mild/moderate Covid-19 there is an 84.1% probability the
risk ratio favours ivermectin. To address concerns expressed about the veracity
of some of the studies we evaluate the sensitivity of the conclusions to any
single study by removing one study at a time. In the worst case, where
(Elgazzar 2020) is removed, the results remain robust, for both severe and mild
to moderate Covid-19. The paper also highlights advantages of using Bayesian
methods over classical statistical methods for meta-analysis. All studies
included in the analysis were prior to data on the delta variant.
