•100% of the
42 studies to date report positive effects.
Random effects meta-analysis for early treatment and pooled effects shows a reduction of 83%, RR
0.17
[0.11-0.28].
Prophylactic use shows a reduction of
89%, RR
0.11
[0.05-0.23].
Mortality results show 75% lower mortality, RR
0.25
[0.14-0.44] for all treatment delays, and
86% lower, RR
0.14
[0.03-0.62] for early treatment.
•100% of the
21 Randomized Controlled Trials (RCTs) report positive effects,
with an estimated reduction of
70%, RR
0.30
[0.19-0.49].
•The probability that an ineffective
treatment generated results as positive as the
42 studies to date is estimated to be 1 in
4 trillion (p = 0.00000000000023).
Figure 1.A. Random effects
meta-analysis excluding late treatment. Simplified dosages are shown for
comparison, these are the total dose in the first two 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 only, for COVID-19 case
results only, and for Randomized Controlled Trials (RCTs) only.
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.
Table 1 summarizes the results.
Treatment time
Number of studies reporting positive results
Total number of studies
Percentage of studies reporting positive results
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.
Results restricted to Randomized Controlled Trials (RCTs) are
shown in Figure 10, 11, 12, and 13, and
Table 2. RCT results are similar to non-RCT results.
Evidence shows that non-RCT trials can also provide reliable results.
[Concato] find that well-designed observational studies do not
systematically overestimate the magnitude of the effects of treatment compared
to RCTs. [Anglemyer] summarized reviews comparing RCTs to
observational studies and found little evidence for significant differences in
effect estimates. [Lee] shows that only 14% of the guidelines of
the Infectious Diseases Society of America were based on RCTs. Evaluation of
studies relies on an understanding of the study and potential biases.
Limitations in an RCT can outweigh the benefits, for example excessive
dosages, excessive treatment delays, or Internet survey bias could have a
greater effect on results. Ethical issues may also prevent running RCTs for
known effective treatments. For more on issues with RCTs see
[Deaton, Nichol].
