HCQ is effective for COVID-19. The probability that an ineffective treatment generated results as positive as the 134 studies to date is estimated to be 1 in 2 billion (p = 0.00000000052).
•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 [0.29-0.46].
•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 0.002.
•There is evidence of bias towards publishing negative results. 88% of prospective studies report positive effects, and only 72% of retrospective studies do.
•Significantly more studies in North America report negative results compared to the rest of the world, p = 0.003.
See the original article [HERE].
“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.”