Bayesian Meta Analysis of Ivermectin Effectiveness in Treating Covid-19 Disease
Neil et al.
, Bayesian Meta Analysis of Ivermectin Effectiveness in Treating Covid-19 Disease
, ResearchGate, doi:0.13140/RG.2.2.31800.88323 (Preprint) (meta analysis)
Bayesian analysis of a subset of ivermectin trial data concluding that there is overwhelming evidence to support a causal link between ivermectin, COVID-19 severity, and mortality.
Currently there are 95 ivermectin studies
and meta analysis shows:
Neil et al., 12 Jul 2021, preprint, 2 authors.
Abstract: See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/353195913
Bayesian Meta Analysis of Ivermectin Effectiveness in Treating Covid-19
Preprint · July 2021
Norman Elliott Fenton
Queen Mary, University of London
Queen Mary, University of London
200 PUBLICATIONS 6,192 CITATIONS
392 PUBLICATIONS 15,653 CITATIONS
Some of the authors of this publication are also working on these related projects:
PAMBAYESIAN (PAtient Managed decision-support using Bayesian networks) View project
CAUSAL-DYNAMICS ("Improved Understanding of Causal Models in Dynamic Decision-making") View project
All content following this page was uploaded by Norman Elliott Fenton on 12 July 2021.
The user has requested enhancement of the
Bayesian Meta Analysis of Ivermectin Effectiveness in
Treating Covid-19 Disease
Martin Neil and Norman Fenton
Risk Information and Management Research
School of Electronic Engineering and Computer Science,
Queen Mary University of London
12 July 2021
A recent peer reviewed meta-analysis evaluating ivermectin (Bryant et al, 2021) concluded
that this antiparasitic drug is a cheap and effective treatment for reducing Covid-19 deaths.
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. 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 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 favours ivermectin; ii) for mild/moderate Covid-19 there is an 84.1% probability the
risk ratio favours ivermectin. Also, from the Bayesian meta-analysis for patients with severe
Covid-19, the mean probability of death without ivermectin treatment is 22.9%, whilst with the
application of ivermectin treatment it is 11.7%. The paper also highlights advantages of using
Bayesian methods over classical statistical methods for meta-analysis.
creative commons license
Please send us corrections, updates, or comments. Vaccines and
treatments are complementary. All practical, effective, and safe means should
be used based on risk/benefit analysis. No treatment, vaccine, or intervention
is 100% available and effective for all current and future variants. We do not
provide medical advice. Before taking any medication, consult a qualified
physician who can provide personalized advice and details of risks and
benefits based on your medical history and situation. FLCCC
provide treatment protocols.