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Hydroxychloroquine as post-exposure prophylaxis for Covid-19: Why simple data analysis can lead to the wrong conclusions from well-designed studies
Luco, J., Trends Med, doi:10.15761/TiM.1000268 (meta analysis)
Luco, Hydroxychloroquine as post-exposure prophylaxis for Covid-19: Why simple data analysis can lead to the wrong.., , J., Trends Med, doi:10.15761/TiM.1000268 (meta analysis)
Sep 2020   Source   PDF  
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Reanalysis of Boulware et al. PEP trial data showing statistically significant improvements with HCQ.
Currently there are 9 HCQ post-exposure prophylaxis studies and meta analysis shows:
Mortality46% lower [-80‑84%]
Hospitalization16% lower [-69‑58%]
Cases24% fewer [0‑41%]
Luco et al., 28 Sep 2020, peer-reviewed, 1 author.
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Abstract: Trends in Medicine Research Article ISSN: 1594-2848 Hydroxychloroquine as post-exposure prophylaxis for Covid-19: Why simple data analysis can lead to the wrong conclusions from well-designed studies Juan M Luco* Department of Chemistry, Faculty of Chemistry, Biochemistry and Pharmacy, National University of San Luis, Chacabuco and Pedernera 5700 San Luis, Argentine Abstract Researchers of the University of Minnesota Medical School reported the first prospective randomized placebo-controlled trial (RCT) in evaluating the role of hydroxychloroquine (HCQ) as post-exposure prophylaxis (PEP) against COVID‐19. The trial's primary result reported by the authors was that, within four days after moderate or high-risk exposure to Covid-19, HCQ did not show benefit over placebo to prevent illnesses compatible with Covid-19 or confirmed infection (P=0.351, Fisher exact test). In this re-analysis, we show why the authors’ oversimplified analysis led to an incorrect conclusion from the data. We re-analyzed the dataset by applying multiple correspondence analysis (MCA) and hierarchical cluster analysis (HCA), which are noise reduction methods used in large data sets. We used the same primary outcome measures as the authors (incidence of COVID-19-compatible disease by day 14) and the same statistical test that the authors used, such as the two-sided Fisher's exact test and others. The results obtained indicate that the individuals' age is a determining factor in the chemopreventive efficacy exerted by HCQ. Thus, in contradiction to the original authors' conclusions, the full data set's risk analysis shows that HCQ exhibits a chemopreventive effect for the group of subjects of ≤ 50 yrs that does not reach significance (P=0.083). However, not considering the analysis of the moderate-risk exposure group, we confirm that the high-risk exposure group (N=719) demonstrates a significant effect of HCQ in the under 50 age group (p=0.025). We also show, using MCA and the Mantel test, systematic differences between the treatment and placebo groups in their clinical characteristics, specifically asthma, and other-comorbidities which act as confounders that add noise to the data, such that the genuine effect of the drug is not seen in a standard analysis. After correcting these differences, the risk analysis showed that HCQ is also useful as a prophylactic agent for people over 50 years of age. This study, therefore, provides evidence of the necessity for higher-order analytics (such as MCA) in the presence of large data sets that include unknown confounders. In this case, it shows that the published conclusion of the group – that HCQ does not prevent COVID-type infective symptoms – was fundamentally flawed and should be reconsidered.
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