Bayesian hypothesis testing and hierarchical modelling of ivermectin effectiveness in treating Covid-19
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.