Yes, HCQ Works Against Covid19

Updated December 2020 is this report from hcqmeta.com HCQ is effective for COVID-19 when used early: meta analysis of 156 studies  (Version 28, December 4, 2020).  Excerpts in italics with my bolds.

HCQ is effective for COVID-19. The probability that an ineffective treatment generated results as positive as the 156 studies to date is estimated to be 1 in 36 trillion (p = 0.000000000000028).

Early treatment is most successful, with 100% of studies reporting a positive effect and an estimated reduction of 65% in the effect measured (death, hospitalization, etc.) using a random effects meta-analysis, RR 0.35 [0.27-0.46].

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 76% of retrospective studies do.

Significantly more studies in North America report negative results compared to the rest of the world, p = 0.0005.

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.

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 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.

Table 1. Results by treatment stage. 2 studies report results for a subset with early treatment, these are not included in the overall results.

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 124 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 65% in the effect measured (death, hospitalization, etc.) in the random effects meta-analysis, RR 0.35 [0.27-0.46]. 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.

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