•98% of the
52 studies to date report positive effects (25 statistically significant in isolation).
Random effects meta-analysis for early treatment and pooled effects shows an 81% reduction, RR
0.19
[0.09-0.38], and
prophylactic use shows
85% improvement, RR
0.15
[0.09-0.25].
Mortality results show 76% lower mortality, RR
0.24
[0.14-0.42] for all treatment delays, and
84% lower, RR
0.16
[0.04-0.63] for early treatment.
•96% of the
27 Randomized Controlled Trials (RCTs) report positive effects,
with an estimated 65% improvement, RR
0.35
[0.24-0.52].
•The probability that an ineffective
treatment generated results as positive as the
52 studies to date is estimated to be 1 in
85 trillion (p = 0.000000000000012).
•All data to reproduce this paper and
the sources are in the appendix. See [Bryant, Hill, Kory, Lawrie, Nardelli] for other meta analyses, all with similar results confirming effectiveness.
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 discussion section. 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
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, and 9 show forest plots for a random effects
meta-analysis of all studies with pooled effects, and for studies reporting
mortality results, COVID-19 case results, and viral clearance results only.
Figure 10 shows results for peer reviewed trials only.
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.
