•Meta analysis using the most serious
outcome reported shows 63% [53‑70%] improvement for the
38 early
treatment studies. Results are similar after exclusion based sensitivity
analysis and after restriction to peer-reviewed studies. The
11 RCTs
show 39% [8‑59%] improvement, and the
15 mortality
results shows 72% [57‑81%] lower mortality.
•21 early treatment
studies show statistically significant improvements in isolation
(15 for the most serious outcome).
•Late treatment is less successful,
with only 67% of the
232 studies reporting a positive effect. Very
late stage treatment is not effective and may be harmful, especially when
using excessive dosages.
•78% of Randomized Controlled Trials (RCTs) for
early, PrEP, or PEP treatment report positive effects, the probability of results
as good or better for an ineffective treatment is
0.0053.
•There is evidence of bias towards publishing negative results.
76% of prospective studies report positive
effects, compared to 71% of retrospective
studies. Studies from North America
are 2.6
times more likely to report negative results than studies from the rest of the
world combined, p = 0.0000000245.
•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.
Multiple treatments are typically used in
combination, which may be significantly more effective.
•No treatment, vaccine, or intervention is 100%
available and effective for all variants. All practical, effective, and safe
means should be used.
Denying the efficacy of treatments increases mortality, morbidity, collateral
damage, and endemic risk.
•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.
We show traditional outcome specific analyses and combined
evidence from all studies, incorporating treatment delay, a primary
confounding factor in COVID-19 studies.
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 and C. Random effects
meta-analysis of all early treatment mortality and hospitalization results.
D. Scatter plot of the effects reported in early treatment studies and
in all studies. Early treatment is more effective. E and F.
Chronological history of all reported effects, with the probability that the
observed or greater frequency of positive results were generated by 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.
An In Vivo animal study supports the efficacy of hydroxychloroquine [Maisonnasse].
3 studies investigate novel formulations of hydroxychloroquine that may be more
effective for COVID-19 [Faísca, Klimke, Zelenko].
[Kavanagh] present a phase I clinical study investigating a
novel formulation of hydroxychloroquine that may be more effective for COVID-19.
Preclinical research is an important part of the development of
treatments, however results may be very different in clinical trials.
Preclinical results are not used in this paper.
Figure 3 shows a visual overview of the results.
Figure 4, Figure 5, and
Table 1 show results by treatment stage, and Figure 6
shows a forest plot for a random effects meta-analysis of all studies.
Figure 7 and Figure 8 show forest plots restricted to mortality
and hospitalization results only.
92% of early treatment studies
report a positive effect, with an estimated reduction of
63% in the effect measured
(death, hospitalization, etc.) from the random effects meta-analysis, RR
0.37
[0.30-0.47].
Late treatment studies are
mixed, with 67% showing positive
effects, and an estimated reduction of
19% 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.
78% of PrEP studies show positive
effects, with an estimated reduction of
31% 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.
Figure 5. Chronological history of results by
treatment stage, with the probability that the observed or greater frequency
of positive results were generated by an ineffective treatment.
