•The probability that an ineffective
treatment generated results as positive as the
60 studies to date is estimated to be 1 in
2 trillion (p = 0.00000000000045).
•Heterogeneity arises from many factors including
treatment delay, population, effect measured, variants, and regimens. The
consistency of positive results is remarkable. Heterogeneity is low in
specific cases, for example early treatment mortality.
•While many treatments have some level
of efficacy, they do not replace vaccines and other measures to avoid
infection. Only 27% of ivermectin
studies show zero events in the treatment arm.
•Elimination of COVID-19 is a race
against viral evolution. No treatment, vaccine, or intervention is 100%
available and effective for all current and future variants. All practical,
effective, and safe means should be used. Not doing so increases the risk of
COVID-19 becoming endemic; and increases mortality, morbidity, and collateral
•Administration with food, often not
specified, may significantly increase plasma and tissue concentration.
•The evidence base is much larger and
has much lower conflict of interest than typically used to approve
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, see the appendix for details. 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 full details 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
frequency of positive results occurred due to random chance from an
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, for studies
within each treatment stage, for mortality results, for COVID-19 case results,
for viral clearance results, for peer-reviewed studies, for 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
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.
Figure 3, 4, and 5 show results by
treatment stage. Figure 6, 7, 8, 9, 10, 11, and 12 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, COVID-19
cases, and viral clearance results only. Figure 13
shows results for peer reviewed trials only. Table 1
summarizes the results.
Number of studies reporting positive effects
Total number of studies
Percentage of studies reporting positive effects
Probability of an equal or greater percentage of positive results from an ineffective