https://hcqmeta.com/

•We analyze all studies for HCQ and COVID-19.
100% of
29 early treatment studies report a positive
effect (13 statistically significant in
isolation), with an estimated reduction of
65% in the effect measured
(death, hospitalization, etc.) using a random effects meta-analysis, RR
0.35
[0.25-0.50].

•92% of Randomized
Controlled Trials (RCTs) for early, PrEP, or PEP treatment report positive
effects, the probability of this happening for an ineffective treatment is
0.0017.

•There is evidence of bias towards
publishing negative results. 88% of prospective
studies report positive effects, and only 73%
of retrospective studies do.

•Studies from North America are
3.8 times more likely to report negative results
than studies from the rest of the world combined,

*p*= 0.0000000015.•Twitter censored our original paper.
This Twitter version contains data only (no conclusions). All data to
reproduce this paper and the sources are in the appendix.

Show forest plot for: | |

All studies | |

Mortality results | |

With exclusions | |

RCTs |

Total | 235 studies | 3,740 authors | 359,862 patients |

Positive effects | 179 studies | 2,743 authors | 251,797 patients |

Early treatment | 65% improvement | RR 0.35 [0.25-0.50] |

Late treatment | 23% improvement | RR 0.77 [0.71-0.83] |

Introduction

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.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.
Figure 6 shows a forest plot restricted to mortality results only.

Early treatment.

100% of early treatment studies
report a positive effect, with an estimated reduction of
65% in the effect measured
(death, hospitalization, etc.) from the random effects meta-analysis, RR
0.35
[0.25-0.50].Late treatment.

Late treatment studies are
mixed, with 72% showing positive
effects, and an estimated reduction of
23% 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.

77% of PrEP studies show positive
effects, with an estimated reduction of
29% 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.

86% of PEP studies report positive
effects, with an estimated reduction of
34% 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 | 30 | 30 | 100% |
0.00000000093
p = 9.3e-10
1 in 1 billion |
65% improvementRR 0.35 [0.25‑0.50] p < 0.0001 |

Late treatment | 113 | 158 | 71.5% |
0.000000031
p = 3.1e-08
1 in 32 million |
23% improvement RR 0.77 [0.71‑0.83] p < 0.0001 |

Pre‑Exposure Prophylaxis | 33 | 43 | 76.7% |
0.0003
p = 0.0003
1 in 3 thousand |
29% improvement RR 0.71 [0.58‑0.87] p = 0.00078 |

Post‑Exposure Prophylaxis | 6 | 7 | 85.7% |
0.062
p = 0.062
1 in 16 |
34% improvement RR 0.66 [0.53‑0.83] p = 0.00043 |

All studies | 179 | 235 | 76.2% |
0.00000000000000018
p = 1.8e-16
1 in 6 quadrillion |
28% improvement RR 0.72 [0.68‑0.77] p < 0.0001 |