•Statistically significant improvements are seen for mortality, ventilation, ICU admission, hospitalization, recovery, cases, and viral clearance.
All remain significant after
exclusions.
56 studies
from 51 independent teams
in 22 different countries
show statistically significant improvements in isolation (38 primary outcome, 36 most serious outcome).
•Results are very robust — in
worst case exclusion sensitivity analysis 56 of 89 studies must be
excluded to avoid finding statistically significant efficacy.
•While many treatments have some level
of efficacy, they do not replace vaccines and other measures to avoid
infection.
Only 24% of ivermectin
studies show zero events in the treatment arm.
Multiple treatments are typically used in
combination, which may be significantly more effective.
•No treatment, vaccine, or intervention is 100%
available and effective for all variants. All practical, effective, and safe
means should be used.
Denying the efficacy of treatments increases mortality, morbidity, collateral
damage, and endemic risk.
•Over 20 countries have adopted ivermectin
for COVID-19. The evidence base is much larger and has much lower conflict of
interest than typically used to approve drugs.
We show traditional outcome specific analyses and combined
evidence from all studies, incorporating treatment delay, a primary
confounding factor in COVID-19 studies.
Figure 1.A. Random effects
meta-analysis excluding late treatment. This plot shows pooled effects, analysis for individual outcomes is below, and
more details on pooled effects can be found in the heterogeneity section.
Effect extraction is pre-specified, using the most serious outcome reported.
Simplified dosages are shown for comparison, these are the total dose in the
first four days for treatment, and the monthly dose for prophylaxis, for a
70kg person. For details of effect extraction and full dosage information
see the appendix.
B. Scatter
plot showing the distribution of effects reported in early treatment studies
and in all studies. C and D. Chronological history of all reported
effects, with the probability that the observed or greater frequency of
positive results were generated by an ineffective treatment.
We analyze all significant studies concerning the use of
ivermectin for COVID-19. Search methods, inclusion criteria, effect extraction
criteria (more serious outcomes have priority), all individual study data,
PRISMA answers, and statistical methods are detailed in Appendix 1. We
present random effects meta-analysis results for all studies, studies within
each treatment stage, specific outcomes, peer-reviewed studies, Randomized
Controlled Trials (RCTs), and after exclusions.
We also perform a simple analysis of the
distribution of study effects. If treatment was not effective, the observed
effects would be randomly distributed (or more likely to be negative if
treatment is harmful). We can compute the probability that the observed
percentage of positive results (or higher) could occur due to chance with an
ineffective treatment (the probability of >= k heads in n coin
tosses, or the one-sided sign test / binomial test). Analysis of publication
bias is important and adjustments may be needed if there is a bias toward
publishing positive results.
Figure 2 shows stages of possible treatment for
COVID-19. Prophylaxis refers to regularly taking medication before
becoming sick, in order to prevent or minimize infection. Early
Treatment refers to treatment immediately or soon after symptoms appear,
while Late Treatment refers to more delayed treatment.
Preclinical research is an important part of the development of
treatments, however results may be very different in clinical trials.
Preclinical results are not used in this paper.
Figure 3 shows a visual overview of the results.
Figure 4, 5, and 6 show results by
treatment stage. Figure 7, 8, 9, 10, 11, 12, 13, and 14
show forest plots for a random effects meta-analysis of all studies with
pooled effects, and for studies reporting mortality results, ICU admission,
mechanical ventilation, hospitalization, recovery, COVID-19 cases, and viral
clearance results only.
Figure 15 shows results for peer reviewed trials only, and the
supplementary data contains peer reviewed and individual outcome results after exclusions.
Table 1 and Table 2 summarize the results.
Figure 5. Chronological history of early and late
treatment results, with the probability that the observed or greater frequency
of positive results were generated by an ineffective treatment.
