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
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引用次数: 3

Abstract

Researchers of the University Minnesota 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.
羟氯喹作为Covid-19暴露后预防措施:为什么简单的数据分析可能从设计良好的研究中得出错误的结论
明尼苏达大学学院的研究人员报告了第一项前瞻性随机安慰剂对照试验(RCT),以评估羟氯喹(HCQ)作为暴露后预防(PEP)对抗COVID - 19的作用。作者报告的试验的主要结果是,在中等或高风险暴露于Covid-19后四天内,HCQ在预防与Covid-19相容的疾病或确诊感染方面没有比安慰剂更好的效果(P=0.351, Fisher精确检验)。在这次重新分析中,我们展示了为什么作者的过度简化的分析导致了数据的错误结论。我们通过多重对应分析(MCA)和层次聚类分析(HCA)重新分析了数据集,这是大数据集中常用的降噪方法。我们使用了与作者相同的主要结局指标(第14天covid -19相容疾病的发病率)和作者使用的相同统计检验,如双侧Fisher精确检验等。结果表明,个体年龄是影响HCQ化学预防效果的决定性因素。因此,与原作者的结论相反,完整数据集的风险分析显示,HCQ在≤50岁的受试者组中具有化学预防作用,但没有达到显著性(P=0.083)。然而,不考虑中等风险暴露组的分析,我们证实,在50岁以下年龄组中,高危暴露组(N=719)对HCQ有显著影响(p=0.025)。我们还显示,使用MCA和Mantel测试,治疗组和安慰剂组在临床特征上的系统差异,特别是哮喘和其他合并症,这些合并症作为混杂因素,给数据增加了噪音,因此在标准分析中看不到药物的真正效果。在纠正了这些差异之后,风险分析表明,HCQ对于50岁以上的人也是一种有效的预防剂。因此,本研究提供了在包含未知混杂因素的大型数据集存在时进行高阶分析(如MCA)的必要性的证据。在这种情况下,这表明该小组发表的结论——HCQ不能预防covid - 19型感染症状——存在根本性缺陷,应予以重新考虑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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