HCQ for COVID-19: real-time meta analysis of 292 studies

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 a possible treatment for COVID-19. Pre-Exposure Prophylaxis (PrEP) refers to regularly taking medication before being infected, to prevent or minimize infection. In PostExposure 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 and Figure 7 show forest plots restricted to mortality and hospitalization results only.


Early treatment.

97% of early treatment studies report a positive effect, with an estimated reduction of 64% in the effect measured (death, hospitalization, etc.) from the random effects meta-analysis, RR 0.36 [0.29-0.46].


Late treatment.

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.


Pre-Exposure Prophylaxis.

75% of PrEP studies show positive effects, with an estimated reduction of 30% 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 risks of these patients at all or do not adjust for the highly variable risk within these patients.





Post-Exposure Prophylaxis.

88% of PEP studies report positive effects, with an estimated reduction of 33% in the random-effects meta-analysis.


Randomized Controlled Trials (RCTs) Randomized Controlled Trials (RCTs) minimize one source of bias and can provide a higher level of evidence. Results restricted to RCTs are shown in Figure 8, Figure 9, and Table 2. Even with the small number of RCTs to date, they confirm efficacy for early treatment. While late treatment RCTs are dominated by the very late stage and large RECOVERY/SOLIDARITY trials, prophylaxis and early treatment studies show 28% improvement in the random-effects meta-analysis, RR 0.72 [0.59‑0.86], p = 0.00053. Early treatment RCTs show 46% improvement, RR 0.54 [0.35‑0.84], p = 0.0058. Evidence supports incorporating non-RCT studies. [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].


Analysis with Exclusions

Many meta-analyses for HCQ have been written, most of which have become somewhat obsolete 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 [GarciaAlbeniz, 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 apply 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 an analysis excluding studies with significant issues, including an indication of significant unadjusted group differences or confounding by indication, extremely late stage usage >14 days post symptoms or >50% on oxygen at baseline, very minimal detail provided, excessive dosages which are 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. The aim here is not to exclude studies on technicalities, but to exclude studies that have major issues that may significantly change the outcome. We welcome feedback on improvements or corrections to this. The studies excluded are as follows, and the resulting forest plot is shown in Figure 10.


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, 75.7% of prospective studies report positive effects, compared to 71.1% of retrospective studies, indicating a bias toward publishing negative results. Figure 12 shows a scatter plot of results for prospective and retrospective studies. Figure 13 shows the results by region of the world, for all regions that have > 5 studies. Studies from North America are 2.7 times more likely to report negative results than studies from the rest of the world combined, 53.5% vs. 20.0%, two-tailed z test -5.41, p = 0.0000000627. [Berry] performed an independent analysis which also showed bias toward negative results for US-based research.


Conclusion

HCQ is an effective treatment for COVID-19. Treatment is more effective when used early. Meta-analysis using the most serious outcome reported shows 64% [54‑71%] improvement for the 32 early treatment studies. Results are similar after exclusion-based sensitivity analysis and after restriction to peer-reviewed studies. Restricting to the 8 RCTs shows 46% [16‑65%] improvement, and restricting to the 13 mortality results shows 75% [60‑84%] lower mortality. Very late-stage treatment is not effective and may be harmful, especially when using excessive dosages. buy hydroxychloroquine | buy hydroxychloroquine online | buy hydroxychloroquine australia | buy hydroxychloroquine canada | buy hydroxychloroquine Amazone | buy hydroxychloroquine Online Singapore |

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