•Results are very robust — in
worst case exclusion sensitivity analysis 53 of 64 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 25% of ivermectin
studies show zero events in the treatment arm.
•Multiple treatments are typically used
in combination, and other treatments could be more effective, including
monoclonal antibodies which may be available in countries not recommending
ivermectin (sotrovimab,
casirivimab/imdevimab, and
bamlanivimab/etesevimab).
•Elimination of COVID-19 is a race
against viral evolution. No treatment, vaccine, or intervention is 100%
available and effective for all variants. All practical, effective, and safe
means should be used, including treatments, as supported by Pfizer [Pfizer, TrialSiteNews].
Denying the efficacy of treatments increases the risk of COVID-19 becoming
endemic; and increases mortality, morbidity, and collateral damage.
•There
is evidence of a negative publication bias, and the probability that an
ineffective treatment generated results as positive as the 64 studies is estimated to be 1 in
222 billion.
•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.
•All data to reproduce this paper and
sources are in the appendix. See
[Bryant, Hariyanto, Kory, Lawrie, Nardelli] for other meta
analyses with similar results confirming efficacy.
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 frequency of positive results
occurred due to random chance from 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, 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 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.
Figure 3, 4, and 5 show results by
treatment stage. Figure 6, 7, 8, 9, 10, 11, 12, and 13
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 14 shows results for peer reviewed trials only, and the
supplementary data contains peer reviewed and individual outcome results after exclusions.
Table 1 summarizes the results.
Treatment time
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
treatment
Figure 4. Chronological history of early and late
treatment results, with the probability that the observed frequency of
positive results occurred due to random chance from an ineffective
treatment.
Figure 6. Random effects meta-analysis for all studies. 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.
