•100% of the
29 early treatment studies report a positive
effect (13 statistically significant in
isolation).
•Random effects meta-analysis with
pooled effects using the most serious outcome reported shows
66% improvement for
the 29 early treatment studies (RR
0.34
[0.24-0.49]). Results are similar
after exclusion based sensitivity analysis:
67% (RR
0.33
[0.24-0.44]),
and after restriction to 21 peer-reviewed studies:
65% (RR
0.35
[0.25-0.47]).
Restricting to the
6 RCTs
shows 46% improvement (RR 0.54
[0.33-0.86]). Restricting to the
13 mortality
results shows 75% lower
mortality (RR 0.25
[0.16-0.40]).
•Late treatment is less successful,
with only 69% of the
183 studies reporting a positive effect. Very
late stage treatment is not effective and may be harmful, especially when
using excessive dosages.
•The probability that an ineffective
treatment generated results as positive as the
265 studies to date is estimated to be 1 in
247 trillion (p = 0.000000000000004).
•87% of Randomized Controlled Trials (RCTs) for
early, PrEP, or PEP treatment report positive effects, the probability of this
happening for an ineffective treatment is
0.0037.
•There is substantial evidence of bias towards publishing negative results.
77% of prospective studies report positive
effects, and only 72% of retrospective
studies do. Studies from North America are 3.1
times more likely to report negative results than studies from the rest of the
world combined, p = 0.0000000066.
•Negative meta analyses of HCQ generally choose a subset
of trials, focusing on late treatment, especially trials with very late
treatment and excessive dosages.
•While many treatments have some level
of efficacy, they do not replace vaccines and other measures to avoid
infection. Only 5% of HCQ 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
damage.
•All data to reproduce this paper and
the sources are in the appendix.
See [Ladapo, Prodromos, Risch, Risch (B)] for other meta analyses
showing efficacy when HCQ is used early.
Figure 1.A. Random effects
meta-analysis of all early treatment 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. Chloroquine is indicated with (c). For details of
effect extraction and full dosage information see the appendix.
B. Scatter plot of the effects reported in early treatment studies and
in all studies. Early treatment is more effective. C and D.
Chronological history of all reported effects, with the probability that the
observed frequency of positive effects occurred due to random chance from an
ineffective treatment.
We analyze all significant studies concerning the use of HCQ
(or CQ) 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, after exclusion of
studies with critical bias, and for Randomized Controlled Trials (RCTs) only.
Typical meta analyses involve subjective selection criteria and bias
evaluation, requiring an understanding of the criteria and the accuracy of the
evaluations. However, the volume of studies presents an opportunity for an
additional simple and transparent analysis aimed at detecting efficacy.
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. For HCQ, we find evidence of a bias toward publishing
negative results.
Figure 2 shows stages of possible treatment for
COVID-19. Pre-Exposure Prophylaxis (PrEP) refers to regularly taking
medication before being infected, in order to prevent or minimize infection.
In Post-Exposure Prophylaxis (PEP), medication is taken after exposure
but before symptoms appear. Early Treatment refers to treatment
immediately or soon after symptoms appear, while Late Treatment refers
to more delayed treatment.
Figure 3, Figure 4, and
Table 1 show results by treatment stage, and Figure 5
shows a forest plot for a random effects meta-analysis of all studies.
Figure 6 and Figure 7 show forest plots restricted to mortality
and hospitalization results only.
100% of early treatment studies
report a positive effect, with an estimated reduction of
66% in the effect measured
(death, hospitalization, etc.) from the random effects meta-analysis, RR
0.34
[0.24-0.49].
Late treatment studies are
mixed, with 69% showing positive
effects, and an estimated reduction of
22% in the random effects
meta-analysis. Negative studies mostly fall into the following categories:
they show evidence of significant unadjusted confounding, including
confounding by indication; usage is extremely late; or they use an excessively
high dosage.
75% of PrEP studies show positive
effects, with an estimated reduction of
30% in the random effects
meta-analysis. Negative studies are all studies of systemic autoimmune disease
patients which either do not adjust for the different baseline risk of these
patients at all, or do not adjust for the highly variable risk within these
patients.
