https://hcqmeta.com/
•HCQ is effective for COVID-19. The
probability that an ineffective treatment generated results as positive as the
148 studies to date is estimated to be 1 in
910 billion (p = 0.0000000000011).
•Early treatment is most successful,
with 100% of studies reporting a
positive effect and an estimated reduction of
63% in the effect measured
(death, hospitalization, etc.) using a random effects meta-analysis, RR
0.37
[0.29-0.47].
•100% of Randomized
Controlled Trials (RCTs) for early, PrEP, or PEP treatment report positive
effects, the probability of this happening for an ineffective treatment is
0.00098.
•There is evidence of bias towards
publishing negative results. 89% of prospective
studies report positive effects, and only 75%
of retrospective studies do.
•Significantly more studies in North
America report negative results compared to the rest of the world, p =
0.001.
Figure 1. A. Scatter plot showing the
distribution of effects reported in early treatment studies and in all studies
(the vertical lines and shaded boxes show the median and interquartile range).
Early treatment is more effective. B and C. Study results ordered by
date, with the line showing the probability that the observed frequency of
positive results occurred due to random chance from an ineffective
treatment.
Introduction
We analyze all significant studies concerning the use of HCQ
(or CQ) for COVID-19, showing the effect size and associated p value
for results comparing to a control group. Methods and study results are
detailed in Appendix 1. Typical meta analyses involve subjective
selection criteria, effect extraction rules, and bias evaluation, requiring an
understanding of the criteria and the accuracy of the evaluations. However,
the volume of studies presents an opportunity for a 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. Treatment stages.
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.
Results
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. Analysis for mortality results only is in Appendix 2,
and analysis excluding studies with major issues can be found in
Appendix 3.
Early treatment.
100% 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
[0.29-0.47].Late treatment.
Late treatment studies are
mixed, with 72% showing positive
effects, and an estimated reduction of
26% 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.Pre-Exposure Prophylaxis.
79% of PrEP studies show positive
effects, with an estimated reduction of
45% 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.Post-Exposure Prophylaxis.
100% of PEP studies report positive
effects, with an estimated reduction of
36% in the random effects
meta-analysis.| 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 | Random effects meta-analysis results |
| Early treatment | 23 | 23 | 100% | 0.00000012 1 in 8 million |
63% improvement RR 0.37 [0.29‑0.47] |
| Late treatment | 68 | 94 | 72.3% | 0.0000087 1 in 115 thousand |
26% improvement RR 0.74 [0.67‑0.82] |
| Pre‑Exposure Prophylaxis | 22 | 28 | 78.6% | 0.0019 1 in 538 |
45% improvement RR 0.55 [0.41‑0.74] |
| Post‑Exposure Prophylaxis | 5 | 5 | 100% | 0.031 1 in 32 |
36% improvement RR 0.64 [0.45‑0.91] |
| All studies | 116 | 148 | 78.4% | 0.0000000000011 1 in 910 billion |
34% improvement RR 0.66 [0.61‑0.72] |
Table 1. Results by treatment stage.
2 studies report results for a subset with early
treatment, these are not included in the overall results.
Figure 3. Results by treatment stage.
Figure 4. Results by treatment stage.
Study results are ordered by date, with the line showing the probability that
the observed frequency of positive results occurred due to random chance from
an ineffective treatment.
Figure 5. Forest plot (random effects model).
(ES) indicates the early treatment subset of a study (these are not included
in the overall results).
Randomized Controlled Trials (RCTs)
RCTs are very valuable and minimize potential bias, however
they are neither necessary or sufficient. [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. Limitations in an RCT can easily outweigh the
benefits, for example excessive dosages, excessive treatment delays, or
Internet survey bias could easily have a greater effect on results. Ethical
issues may prevent running RCTs for known effective treatments. For more on
the problems with RCTs see [Deaton, Nichol]. Results restricted to RCTs
are shown in Figure 6, Figure 7, and Table 2.
Even with the small number of RCTs to date, there is a strong indication of
efficacy. When excluding late treatment,
100% of RCTs to date report positive
results.
Figure 6. Randomized Controlled Trials. The
distribution of results for RCTs is similar to the distribution for all other
studies.
| 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 | Random effects meta-analysis results |
| Randomized Controlled Trials | 19 | 23 | 82.6% | 0.0013 1 in 769 |
10% improvement RR 0.90 [0.78‑1.04] |
| Randomized Controlled Trials (excluding late treatment) | 10 | 10 | 100% | 0.00098 1 in 1 thousand |
10% improvement RR 0.90 [0.78‑1.04] |
Table 2. Summary of RCT results.
Discussion
Publication bias.
Publishing is often biased
towards positive results, which we would need to adjust for when analyzing the
percentage of positive results. Studies that require less effort are
considered to be more susceptible to publication bias. Prospective trials that
involve significant effort are likely to be published regardless of the
result, while retrospective studies are more likely to exhibit bias. For
example, researchers may perform preliminary analysis with minimal effort and
the results may influence their decision to continue. Retrospective studies
also provide more opportunities for the specifics of data extraction and
adjustments to influence results.For HCQ, 89.5% of prospective
studies report positive effects, compared to
74.5% of retrospective studies, indicating a
bias toward publishing negative results. Figure 8 shows a scatter
plot of results for prospective and retrospective studies.
Figure 9 shows the results by region of the world, for
all regions that have > 5 studies. Studies from North America are
significantly more likely to report negative results than studies from the
rest of the world combined, two-tailed z test
-3.26, p = 0.001.
[Berry] performed an independent analysis which also showed bias
toward negative results for US-based research.
Figure 8. Prospective vs. retrospective studies.
Figure 9. Results by region.
The lack of bias towards positive results is not very
surprising. Both negative and positive results are very important given the
current use of HCQ for COVID-19 around the world, evidence of which can be
found in the studies analyzed here, government protocols, and news reports,
for example [AFP, AfricaFeeds, Africanews, Afrik.com, Al Arabia, Al-bab, Anadolu Agency, Anadolu Agency (B), Archyde, Barron's, Barron's (B), BBC, Belayneh, A., Bianet, CBS News, Challenge, Dr. Goldin, Efecto Cocuyo, Expats.cz, Face 2 Face Africa, France 24, France 24 (B), Franceinfo, Global Times, Government of China, Government of India, Government of Venezuela, GulfInsider, Le Nouvel Afrik, LifeSiteNews, Medical World Nigeria, Medical Xpress, Medical Xpress (B), Middle East Eye, Ministerstva Zdravotnictví, Ministry of Health of Ukraine, Ministry of Health of Ukraine (B), Morocco World News, Mosaique Guinee, Nigeria News World, NPR News, Oneindia, Pan African Medical Journal, Parola, Pilot News, PledgeTimes, Pleno.News, Q Costa Rica, Rathi, Russian Government, Russian Government (B), Teller Report, The Africa Report, The Australian, The BL, The East African, The Guardian, The Indian Express, The Moscow Times, The North Africa Post, The Tico Times, Ukrinform, Vanguard, Voice of America].
We also note a bias towards publishing negative results by
certain journals and press organizations, with scientists reporting difficulty
publishing positive results [Boulware, Meneguesso]. Although
116 studies show positive results, The New York Times, for example,
has only written articles for studies that claim HCQ is not effective
[The New York Times, The New York Times (B), The New York Times (C)]. As of September 10, 2020, The New
York Times still claims that there is clear evidence that HCQ is not effective
for COVID-19 [The New York Times (D)]. As of October 9, 2020, the United
States National Institutes of Health recommends against HCQ for both
hospitalized and non-hospitalized patients [United States National Institutes of Health].
Treatment details.
We focus here on the
question of whether HCQ is effective or not for COVID-19. Significant
differences exist based on treatment stage, with early treatment showing the
greatest effectiveness. 100% of
early treatment studies report a positive effect, with an estimated reduction
of 63% in the effect measured
(death, hospitalization, etc.) in the random effects meta-analysis, RR
0.37
[0.29-0.47]. Many factors are likely to
influence the degree of effectiveness, including the dosing regimen,
concomitant medications such as zinc or azithromycin, precise treatment delay,
the initial viral load of patients, and current patient conditions.Conclusion
HCQ is an effective treatment for
COVID-19. The probability that an ineffective treatment generated results as
positive as the 148 studies to date is estimated
to be 1 in 910 billion (p =
0.0000000000011).
Revisions
This paper is data driven, all graphs and numbers are
dynamically generated. We will update the paper as new studies are released or
with any corrections.
10/21: We added studies [Dubee, Martinez-Lopez, Solh]. We
received a report that the United States National Institutes of Health is
recommending against HCQ for hospitalized and non-hospitalized patients as of
October 9, and we added a reference.
10/22: We added [Anglemyer, Ñamendys-Silva]. We updated
the discussion of [Axfors] for the second version of this study. We
added a table summarizing RCT results.
10/23: We added [Komissarov, Lano]. The second version
of the preprint for [Komissarov] includes a comparison with the
control group (not reported in the first version). We updated
[Lyngbakken] to use the mortality result in the recent journal version
of the paper (not reported in the preprint).
10/30: We added [Berenguer, Faíco-Filho].
11/1: We added [Trullàs].
11/8: We added [Dhibar].
11/9: We added [Self].
11/10: We added [Mathai].
11/12: We added [Simova, Simova (B)].
11/13: We added [Núñez-Gil, Águila-Gordo].
11/14: We added [Sheshah].
11/18: We added [Budhiraja].
11/19: We added [Falcone].
11/20: We added [Omrani].
11/23: We added [Revollo].
11/24: We added [Boari].
11/25: We added [Qin], and we added analysis
restricted to mortality results.
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We performed ongoing searches of PubMed, medRxiv,
ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research
Square, ScienceDirect, Oxford University Press, the reference lists of other
studies and meta-analyses, and submissions to the site c19study.com, which regularly receives
submissions of both positive and negative studies upon publication. Search
terms were hydroxychloroquine or chloroquine and COVID-19 or SARS-CoV-2, or
simply hydroxychloroquine or chloroquine. Automated searches are performed
every hour with notifications of new matches. All studies regarding the use of
HCQ or CQ for COVID-19 that report an effect compared to a control group are
included in the main analysis. This is a living analysis and is updated
regularly.
We extracted effect sizes and associated data from all studies.
If studies report multiple kinds of effects then the most serious outcome is
used in calculations for that study. For example, if effects for mortality and
cases are both reported, the effect for mortality is used, this may be
different to the effect that a study focused on. If symptomatic results are
reported at multiple times, we used the latest time, for example if mortality
results are provided at 14 days and 28 days, the results at 28 days are used.
Mortality alone is preferred over combined outcomes. Outcomes with zero events
in both arms were not used. Clinical outcome is considered more important than
PCR testing status. For PCR results reported at multiple times, preference is
given to results mid-recovery (after most or all patients have recovered there
is no room for an effective treatment to do better). When results provide an
odds ratio, we computed the relative risk when possible, or converted to a
relative risk according to [Zhang]. Reported confidence intervals
and p-values were used when available, using adjusted values when
provided. If multiple types of adjustments are reported including propensity
score matching (PSM), the PSM results are used. When needed, conversion
between reported p-values and confidence intervals followed
[Altman, Altman (B)], and Fisher's exact test was used to calculate
p-values for event data. If continuity correction for zero values are
required, we use the reciprocal of the opposite arm with the sum of the
correction factors equal to 1 [Sweeting]. If a study separated HCQ and
HCQ+AZ we used the combined results were possible, or the results for the
larger group. Results are all expressed with RR < 1.0 suggesting
effectiveness. Most results are the relative risk of something negative. A few
studies report relative times, where the results are expressed as the ratio of
the time for the HCQ group versus the time for the control group. One study
reports the rate of reduction of viral load, where the result is based on the
percentage change in the rate. Calculations were done in Python (3.8.5)
with
scipy (1.3.3), pythonmeta (1.11), numpy (1.19.1), statsmodels (0.12.0), and plotly (4.10.0).
The forest plots are computed using PythonMeta [Deng] with the
DerSimonian and Laird random effects model (the fixed effect assumption is not
plausible in this case). We received no funding, this research is done in our
spare time. We have no affiliations with any pharmaceutical companies or
political parties.
We have classified studies as early treatment if most patients
are not already at a severe stage at the time of treatment, and treatment
started within 5 days after symptoms, although a shorter time may be
preferable. Antivirals are typically only considered effective when used
within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir,
with longer delays not being effective [McLean, Treanor].
A summary of study results is below. It is easy to propose
excluding certain papers for various reasons, for example [Fried, Kelly, Kuderer, McGrail]
report negative results but do not themselves consider the results comparable
- they note that treated patients were significantly more ill and do not make
adjustments. To avoid potential bias in evaluation we currently include all
studies. HCQ research exhibits a negative bias as shown above and addressing
this bias will increase the observed efficacy. Given the state of scientific
discussion about HCQ, we feel that a conservative approach is appropriate,
especially since efficacy is clear even with this approach. For reference, a
draft analysis excluding studies with major issues can be found in
Appendix 3.
Please submit updates and corrections with the form at
https://hcqmeta.com/.
Only one result per study is included in calculations, as per
the details above.
[Abella],
risk of COVID-19 case,
RR 0.95, p = 1.00.
[Arleo],
all patients,
RR 0.50, p = 0.67.
[Arleo],
inpatients,
RR 0.48, p = 0.64.
[Behera],
risk of COVID-19 case,
RR 0.72, p = 0.29.
[Bhattacharya],
risk of COVID-19 case,
RR 0.19, p = 0.001.
[Cassione],
risk of COVID-19 case,
RR 1.50, p = 0.59.
[Chatterjee],
full course vs. unused risk of COVID-19 case,
RR 0.33, p < 0.001.
[de la Iglesia],
risk of hospitalization,
RR 1.50, p = 1.00.
[de la Iglesia],
suspected COVID-19,
RR 1.43, p = 0.15.
[de la Iglesia],
confirmed COVID-19,
RR 0.92, p = 0.84.
[Ferreira],
risk of COVID-19 case,
RR 0.53, p < 0.001.
[Ferri],
risk of COVID-19 case,
RR 0.37, p = 0.01.
[Gendebien],
risk of COVID-19 case,
RR 0.96, p = 0.93.
[Gendelman],
risk of COVID-19 case,
RR 0.92, p = 0.88.
[Gentry],
risk of death,
RR 0.13, p = 0.10.
[Gentry],
risk of COVID-19 case,
RR 0.79, p = 0.27.
[Gianfrancesco],
risk of hospitalization,
RR 0.97, p = 0.82.
[Goenka],
risk of IgG positive,
RR 0.13, p = 0.03.
[Grau-Pujol],
risk of COVID-19 case,
RR 0.32, p = 0.47.
[Huang],
risk of hospitalization,
RR 0.20, p < 0.001.
[Huh],
risk of COVID-19 case,
RR 1.48, p = 0.09.
[Khurana],
risk of COVID-19 case,
RR 0.49, p = 0.02.
[Konig],
risk of hospitalization,
RR 0.97, p = 0.88.
[Laplana],
risk of COVID-19 case,
RR 1.56, p = 0.24.
[Macias],
risk of hospitalization,
RR 0.74, p = 1.00.
[Macias],
risk of COVID-19 case,
RR 1.49, p = 0.53.
[Mathai],
risk of COVID-19 case,
RR 0.10, p < 0.001.
[Mathai],
risk of COVID-19 case,
RR 0.12, p < 0.001, symptomatic.
[Mitchell],
risk of death,
RR 0.01, p < 0.001.
[Rajasingham],
risk of hospitalization,
RR 0.50, p = 1.00.
[Rajasingham],
risk of COVID-19 case,
RR 0.73, p = 0.12.
[Rentsch],
risk of death,
RR 1.03, p = 0.83.
[Revollo],
PSM risk of PCR+,
RR 0.77, p = 0.52.
[Revollo],
PSM risk of IgG+,
RR 1.43, p = 0.42.
[Singer],
risk of COVID-19 case,
RR 1.09, p = 0.62.
[Zhong],
risk of COVID-19 case,
RR 0.09, p = 0.04.
Only one result per study is included in calculations, as per
the details above.
[Boulware (B)],
risk of COVID-19 case,
RR 0.83, p = 0.35.
[Boulware (B)],
probable COVID-19 case,
RR 0.75, p = 0.22.
[Dhibar],
risk of COVID-19 case,
RR 0.59, p = 0.03.
[Dhibar],
risk of COVID-19 case,
RR 0.50, p = 0.04, PCR+.
[Dhibar],
risk of symptomatic case,
RR 0.56, p = 0.21.
[Mitjà],
risk of death,
RR 0.68, p = 0.58.
[Mitjà],
baseline pcr- risk of cases,
RR 0.68, p = 0.27.
[Polat],
risk of COVID-19 case,
RR 0.43, p = 0.03.
[Simova],
risk of COVID-19 case,
RR 0.07, p = 0.01.
Only one result per study is included in calculations, as per
the details above.
[Ashraf],
risk of death,
RR 0.32, p = 0.15.
[Cadegiani],
risk of death,
RR 0.19, p = 0.21, control group 1.
[Cadegiani],
risk of ventilation,
RR 0.05, p < 0.001, control group 1.
[Cadegiani],
risk of hospitalization,
RR 0.02, p < 0.001, control group 1.
[Chen],
median time to PCR-,
RR 0.28, p = 0.01.
[Derwand],
risk of death,
RR 0.21, p = 0.12.
[Derwand],
risk of hospitalization,
RR 0.18, p < 0.001.
[Esper],
risk of hospitalization,
RR 0.36, p = 0.02.
[Fonseca],
HCQ vs. nothing,
RR 0.36, p < 0.001.
[Fonseca],
HCQ vs. anything else,
RR 0.49, p = 0.006.
[Gautret],
risk of no virological cure at day 6,
RR 0.34, p = 0.001.
[Guisado-Vasco],
risk of death,
RR 0.12, p = 0.001.
[Guérin],
risk of death,
RR 0.39, p = 1.00.
[Guérin],
risk of no recovery,
RR 0.35, p < 0.001.
[Heras],
risk of death,
RR 0.08, p < 0.001.
[Hong],
risk of prolonged viral shedding,
RR 0.35, p = 0.001.
[Huang (B)],
risk of no virological cure,
RR 0.41, p < 0.001.
[Huang (C)],
risk of no recovery at day 14,
RR 0.08, p = 0.02.
[Huang (C)],
risk of no improvement in pneumonia at day 14,
RR 0.17, p = 0.22.
[Ip],
risk of hospitalization,
RR 0.54, p = 0.03.
[Izoulet],
risk of death,
RR 0.15, p < 0.001.
[Kirenga],
median time to recovery,
RR 0.74, p = 0.20.
[Lagier],
risk of death,
RR 0.41, p = 0.05.
[Ly],
risk of death,
RR 0.44, p = 0.02.
[Mitjà (B)],
risk of hospitalization,
RR 0.75, p = 0.64.
[Mitjà (B)],
risk of no recovery,
RR 0.83, p = 0.38.
[Omrani],
HCQ+AZ or HCQ vs. control risk of hospitalization,
RR 0.88, p = 1.00.
[Omrani],
HCQ+AZ or HCQ vs. control risk of symptomatic at day 21,
RR 0.74, p = 0.58.
[Omrani],
HCQ+AZ or HCQ vs. control risk of Ct<=40 at day 14,
RR 1.10, p = 0.13.
[Simova (B)],
risk of hospitalization,
RR 0.06, p = 0.01.
[Simova (B)],
risk of viral+ at day 14,
RR 0.04, p = 0.001.
[Skipper],
risk of hospitalization,
RR 0.48, p = 0.19.
[Skipper],
risk of no recovery at day 14,
RR 0.80, p = 0.21.
[Sulaiman],
risk of death,
RR 0.36, p = 0.01.
[Sulaiman],
risk of hospitalization,
RR 0.61, p = 0.001.
Only one result per study is included in calculations, as per
the details above.
[Abd-Elsalam],
risk of death,
RR 1.20, p = 1.00.
[Abd-Elsalam],
risk of no recovery at day 28,
RR 0.70, p = 0.009.
[Alamdari],
risk of death,
RR 0.45, p = 0.03.
[Alberici],
risk of death,
RR 0.57, p = 0.12.
[Almazrou],
risk of ventilation,
RR 0.35, p = 0.16.
[Almazrou],
risk of ICU admission,
RR 0.79, p = 0.78.
[An],
time to viral clearance,
RR 0.97, p = 0.92.
[Annie],
risk of death,
RR 0.96, p = 0.83.
[Annie],
risk of death,
RR 1.21, p = 0.46.
[Aparisi],
risk of death,
RR 0.37, p = 0.008.
[Arshad],
risk of death,
RR 0.49, p = 0.009.
[Ashinyo],
risk of hospitalization,
RR 0.67, p = 0.03.
[Ayerbe],
risk of death,
RR 0.48, p < 0.001.
[Barbosa],
risk of death,
RR 2.47, p = 0.58.
[Berenguer],
risk of death,
RR 0.38, p < 0.001.
[Bernaola],
risk of death,
RR 0.83, p < 0.001.
[Boari],
risk of death,
RR 0.45, p < 0.001.
[Bousquet],
risk of death,
RR 0.57, p = 0.15.
[Budhiraja],
risk of death,
RR 0.35, p < 0.001.
[Catteau],
risk of death,
RR 0.68, p < 0.001.
[Cavalcanti],
HCQ+HCQ/AZ risk of death,
RR 0.84, p = 0.77.
[Cavalcanti],
HCQ+HCQ/AZ risk of hospitalization,
RR 1.28, p = 0.30.
[Chen (B)],
risk of no virological cure,
RR 0.76, p = 0.71.
[Chen (B)],
median time to PCR-,
RR 0.50, p = 0.40.
[Chen (C)],
risk of no virological cure,
RR 1.29, p = 0.70.
[Chen (D)],
risk of no improvement in pneumonia at day 6,
RR 0.43, p = 0.04.
[Chen (E)],
risk of radiological progression,
RR 0.71, p = 0.57.
[Chen (E)],
risk of viral+ at day 7,
RR 2.00, p = 1.00.
[Choi],
median time to PCR-,
RR 1.22, p < 0.001.
[Coll],
risk of death,
RR 0.54, p < 0.001.
[Cravedi],
risk of death,
RR 1.53, p = 0.17.
[D'Arminio Monforte],
risk of death,
RR 0.66, p = 0.12.
[Davido],
risk of combined intubation/hospitalization,
RR 0.45, p = 0.04.
[Di Castelnuovo],
risk of death,
RR 0.70, p < 0.001.
[DISCOVERY],
29 day mortality estimated from graph,
RR 0.69, p = 0.35.
[DISCOVERY],
risk of 7-point scale status,
RR 0.83, p = 0.40.
[Dubee],
mortality at day 28,
RR 0.54, p = 0.21.
[Dubee],
combined mortality/intubation at day 28,
RR 0.74, p = 0.82.
[Dubee],
HCQ+AZ from day 0 subgroup combined mortality/intubation,
RR 0.15, p = 0.21.
[Dubernet],
risk of ICU admission,
RR 0.12, p = 0.008.
[Falcone],
risk of death,
RR 0.35, p = 0.20, PSM.
[Falcone],
risk of death,
RR 0.75, p = 0.36, multivariate Cox regression.
[Falcone],
risk of death,
RR 0.43, p < 0.001, univariate Cox regression.
[Faíco-Filho],
Δt7-12 ΔCt improvement,
RR 0.19, p = 0.40.
[Faíco-Filho],
Δt<7 ΔCt improvement,
RR 0.76, p = 0.36.
[Faíco-Filho],
Δt>12 ΔCt improvement,
RR 1.15, p = 0.52.
[Fontana],
risk of death,
RR 0.50, p = 0.53.
[Fried],
risk of death,
RR 1.27, p < 0.001.
[Frontera],
PSM,
RR 0.63, p = 0.01.
[Frontera],
regression,
RR 0.76, p = 0.02.
[Geleris],
risk of combined intubation/death,
RR 1.04, p = 0.76.
[Goldman],
risk of death,
RR 0.78, p = 0.46.
[Gonzalez],
risk of death,
RR 0.73, p = 0.06.
[Guisado-Vasco (B)],
risk of death,
RR 0.80, p = 0.36.
[Gupta],
risk of death,
RR 1.06, p = 0.41.
[Heberto],
risk of death,
RR 0.46, p = 0.04.
[Heberto],
risk of ventilation,
RR 0.34, p = 0.008.
[Huang (D)],
risk of no virological cure,
RR 0.33, p < 0.001.
[Ip (B)],
risk of death,
RR 0.99, p = 0.93.
[Kamran],
risk of disease progression,
RR 0.95, p = 1.00.
[Kamran],
with comorbidities,
RR 0.45, p = 0.30.
[Kamran],
risk of viral+ at day 14,
RR 1.10, p = 0.52.
[Kelly],
risk of death,
RR 2.43, p = 0.03.
[Kim],
risk of hospitalization,
RR 0.49, p = 0.01.
[Kim],
risk of no virological cure,
RR 0.44, p = 0.005.
[Komissarov],
risk of viral load,
RR 1.25, p = 0.45.
[Kuderer],
risk of death,
RR 2.34, p < 0.001, HCQ+AZ.
[Lammers],
risk of combined death/ICU,
RR 0.68, p = 0.02.
[Lano],
risk of death,
RR 0.67, p = 0.28.
[Lano],
risk of combined death/ICU,
RR 0.61, p = 0.23.
[Lano],
not requiring O2 on diagnosis,
RR 0.31, p = 0.11.
[Lauriola],
risk of death,
RR 0.27, p < 0.001.
[Lecronier],
risk of death,
RR 0.58, p = 0.24, HCQ vs. control.
[Lecronier],
risk of treatment escalation,
RR 0.94, p = 0.73, HCQ vs. control.
[Lecronier],
risk of viral+ at day 7,
RR 0.85, p = 0.61, HCQ vs. control.
[Luo],
risk of death,
RR 1.02, p = 0.99.
[Lyngbakken],
risk of death,
RR 0.96, p = 1.00.
[Lyngbakken],
improvement in viral load reduction rate,
RR 0.29, p = 0.51.
[Magagnoli],
risk of death,
RR 1.31, p = 0.28.
[Mahévas],
risk of death,
RR 1.20, p = 0.75.
[Martinez-Lopez],
risk of death,
RR 0.67, p = 0.20.
[McGrail],
risk of death,
RR 1.70, p = 0.69.
[Membrillo de Novales],
risk of death,
RR 0.45, p = 0.002.
[Mikami],
risk of death,
RR 0.53, p < 0.001.
[Nachega],
risk of death,
RR 0.72, p = 0.17.
[Nachega],
risk of no improvement,
RR 0.74, p = 0.13.
[Núñez-Gil],
risk of death,
RR 0.92, p = 0.005.
[Paccoud],
risk of death,
RR 0.89, p = 0.88.
[Peters],
risk of death,
RR 1.09, p = 0.57.
[Pinato],
risk of death,
RR 0.41, p < 0.001.
[Qin],
risk of death,
RR 0.66, p = 0.61.
[RECOVERY],
risk of death,
RR 1.09, p = 0.15.
[Rivera],
risk of death,
RR 1.02, p = 0.90.
[Roomi],
risk of death,
RR 1.38, p = 0.54.
[Rosenberg],
risk of death,
RR 1.35, p = 0.31.
[Saleemi],
median time to PCR-,
RR 1.21, p < 0.05.
[Sbidian],
risk of death,
RR 1.05, p = 0.74, whole population HCQ AIPTW adjusted.
[Sbidian],
risk of no hospital discharge,
RR 0.80, p = 0.002, whole population HCQ AIPTW adjusted.
[Self],
risk of death,
RR 0.93, p = 0.84.
[Serrano],
risk of death,
RR 0.57, p = 0.14.
[Shabrawishi],
risk of no virological cure at day 5,
RR 0.85, p = 0.66.
[Sheshah],
risk of death,
RR 0.20, p < 0.001.
[Shoaibi],
risk of death,
RR 0.85, p < 0.001.
[Singh],
risk of death,
RR 0.95, p = 0.72.
[Singh],
risk of ventilation,
RR 0.81, p = 0.26.
[Solh],
risk of death,
RR 1.18, p = 0.17.
[SOLIDARITY],
risk of death,
RR 1.19, p = 0.23.
[Soto-Becerra],
risk of death,
RR 1.84, p = 0.02.
[Synolaki],
risk of death,
RR 0.76, p = 0.27.
[Sánchez-Álvarez],
risk of death,
RR 0.54, p = 0.005.
[Tang],
risk of no virological cure at day 21,
RR 0.79, p = 0.51.
[Tehrani],
risk of death,
RR 0.87, p = 0.63.
[Trullàs],
risk of death,
RR 0.64, p = 0.12.
[Ulrich],
risk of death,
RR 1.06, p = 1.00.
[Wang],
risk of death,
RR 0.94, p = 0.63.
[Xia],
risk of no virological cure,
RR 0.62, p = 0.17.
[Yu],
risk of death,
RR 0.40, p = 0.002.
[Zhong (B)],
risk of no virological cure at day 10,
RR 0.20, p < 0.001.
[Águila-Gordo],
risk of death,
RR 0.33, p = 0.10.
[Ñamendys-Silva],
HCQ+AZ vs. neither HCQ or CQ,
RR 0.68, p = 0.18.
[Ñamendys-Silva],
CQ vs. neither HCQ or CQ,
RR 0.63, p = 0.09.
[Ñamendys-Silva],
HCQ+AZ or CQ,
RR 0.66, p = 0.006.
Figure 10 shows a forest plot restricted to mortality results only.
Figure 10. Forest plot (random effects model) for
mortality results only. (ES) indicates the early treatment subset of a study
(these are not included in the overall results).
Many meta-analyses for HCQ have been written, most of which
have become somewhat obselete due to the continuing stream of more recent
studies. Recent analyses with positive conclusions include [IHU Marseille]
which considers significant bias from an understanding of each trial, and
[Garcia-Albeniz, Ladapo, Prodromos] which focus on early or
prophylactic use studies.
Meta analyses reporting negative conclusions focus on late
treatment studies, tend to disregard treatment delay, tend to follow formulaic
evaluations which overlook major issues with various studies, and end up with
weighting disproportionate to a reasoned analysis of each study's
contribution. For example, [Axfors] assigns 87% weight to a single
trial, the RECOVERY trial [RECOVERY], thereby producing the same
result. However, the RECOVERY trial may be the most biased of the studies they
included, due to the excessive dosage used, close to the level shown to be
very dangerous in [Borba] (OR 2.8), and with extremely sick late stage
patients (60% requiring oxygen, 17% ventilation/ECMO, and a very high
mortality rate in both arms). There is little reason to suggest that the
results from this trial are applicable to more typical dosages or to earlier
treatment (10/22: the second version of this study released 10/22 assigns 74%
to RECOVERY and 15% to SOLIDARITY [SOLIDARITY], which is the only
other trial using a similar excessive dosage).
We include all studies in the main analysis, however there are
major issues with several studies that could significantly alter the results.
Here, we present a draft analysis excluding studies with significant issues,
including indication of significant unadjusted group differences or confouding
by indication, extremely late stage usage >14 days post symptoms or >50% on
oxygen at baseline, very minimal detail provided, excessive dosages which have
been shown to be dangerous, significant issues with adjustments that could
reasonably make substantial differences, and reliance on PCR which may be
inaccurate and less indicative of severity than symptoms. We welcome feedback
on improvements or corrections to this. The studies excluded are as follows,
and the resulting forest plot is shown in Figure 11.
[Alamdari], substantial unadjusted confounding by indication.
[An], results only for PCR status which may be significantly different to symptoms.
[Annie], confounding by indication is likely and adjustments do not consider COVID-19 severity.
[Barbosa], excessive unadjusted differences between groups.
[Budhiraja], excessive unadjusted differences between groups.
[Cassione], not fully adjusting for the different baseline risk of systemic autoimmune patients.
[Chen], results only for PCR status which may be significantly different to symptoms.
[Chen (B)], results only for PCR status which may be significantly different to symptoms.
[Chen (C)], results only for PCR status which may be significantly different to symptoms.
[Cravedi], substantial unadjusted confounding by indication.
[de la Iglesia], not fully adjusting for the different baseline risk of systemic autoimmune patients.
[Fried], excessive unadjusted differences between groups, substantial unadjusted confounding by indication.
[Gautret], excessive unadjusted differences between groups, results only for PCR status which may be significantly different to symptoms.
[Geleris], significant issues found with adjustments.
[Gendebien], not fully adjusting for the baseline risk differences within systemic autoimmune patients.
[Gendelman], not fully adjusting for the different baseline risk of systemic autoimmune patients.
[Gianfrancesco], not fully adjusting for the baseline risk differences within systemic autoimmune patients.
[Gupta], >50% on oxygen/ventilation at baseline.
[Hong], results only for PCR status which may be significantly different to symptoms.
[Huang], significant unadjusted confounding possible.
[Huang (B)], results only for PCR status which may be significantly different to symptoms.
[Huang (D)], results only for PCR status which may be significantly different to symptoms.
[Huh], not fully adjusting for the different baseline risk of systemic autoimmune patients.
[Izoulet], excessive unadjusted differences between groups.
[Kamran], excessive unadjusted differences between groups.
[Kelly], substantial unadjusted confounding by indication.
[Konig], not fully adjusting for the baseline risk differences within systemic autoimmune patients.
[Kuderer], substantial unadjusted confounding by indication.
[Laplana], not fully adjusting for the different baseline risk of systemic autoimmune patients.
[Lecronier], >50% on oxygen/ventilation at baseline.
[Luo], substantial unadjusted confounding by indication.
[Lyngbakken], results only for PCR status which may be significantly different to symptoms.
[Macias], not fully adjusting for the baseline risk differences within systemic autoimmune patients.
[McGrail], excessive unadjusted differences between groups.
[Mitchell], excessive unadjusted differences between groups.
[Peters], excessive unadjusted differences between groups.
[RECOVERY], excessive dosage, results do not apply to typical dosages.
[Rentsch], not fully adjusting for the baseline risk differences within systemic autoimmune patients, medication adherence unknown and may significantly change results.
[Roomi], substantial unadjusted confounding by indication.
[Saleemi], results only for PCR status which may be significantly different to symptoms, substantial unadjusted confounding by indication.
[Sbidian], significant issues found with adjustments.
[Shabrawishi], results only for PCR status which may be significantly different to symptoms.
[Singer], not fully adjusting for the baseline risk differences within systemic autoimmune patients.
[Singh], confounding by indication is likely and adjustments do not consider COVID-19 severity.
[Solh], >50% on oxygen/ventilation at baseline, substantial unadjusted confounding by indication.
[SOLIDARITY], excessive dosage, results do not apply to typical dosages, >50% on oxygen/ventilation at baseline.
[Soto-Becerra], confounding by indication is likely and adjustments do not consider COVID-19 severity.
[Tang], results only for PCR status which may be significantly different to symptoms.
[Tehrani], substantial unadjusted confounding by indication.
[Ulrich], >50% on oxygen/ventilation at baseline.
[Wang], confounding by indication is likely and adjustments do not consider COVID-19 severity.
[Xia], detail too minimal.
[Zhong (B)], results only for PCR status which may be significantly different to symptoms.
Figure 11. Forest plot (random effects model)
excluding studies with significant issues. (ES) indicates the early treatment
subset of a study (these are not included in the overall results).
Please send us corrections, updates, or comments. Please also send us
examples of censorship and bias regarding publication and sharing of HCQ
research, and of influential people or organizations that claim HCQ is
ineffective.
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