Rational for meta-analysis and randomized treatment: the COVID-19 example

Raoult, D., Clinical Microbiology and Infection, doi:10.1016/j.cmi.2020.10.012, Jan 2021
HCQ for COVID-19
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Meta Analysis
Discussion of the analysis of studies evaluating hydroxychloroquine for COVID-19. Author argues that several critical factors are often overlooked in clinical studies: confirmation of diagnosis, disease stage at treatment initiation, drug dosage and duration, adequate treatment time, objective outcome measures like death and viral clearance, and comparison of high-risk groups. Hydroxychloroquine studies yield contradictory results because these factors are inconsistently applied. Author challenges the dogma that randomized controlled trials are superior to observational studies, citing a Cochrane Library review of 1583 meta-analyses that failed to show such superiority. Meta-analyses themselves demonstrate discrepancies between different randomized studies, proving that these studies do not eliminate biases. Author advocates for neutral organizations without conflicts of interest to process data and publish papers, with data made available within a year for evaluation by other teams.
Reviews covering hydroxychloroquine for COVID-19 include1-30.
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Raoult et al., 31 Jan 2021, France, peer-reviewed, 1 author. Contact: didier.raoult@gmail.com.
Abstract: Clinical Microbiology and Infection 27 (2021) 6e8 Contents lists available at ScienceDirect Clinical Microbiology and Infection journal homepage: www.clinicalmicrobiologyandinfection.com Commentary Rational for meta-analysis and randomized treatment: the COVID-19 example Didier Raoult 1, 2, * 1) 2) MEPHI, IRD, Aix Marseille Univ, AP-HM, Marseille, France IHU-M editerran ee Infection, Marseille, France a r t i c l e i n f o Article history: Received 21 September 2020 Received in revised form 9 October 2020 Accepted 13 October 2020 Available online 21 October 2020 Editor: L. Leibovici My opinion is that hydroxychloroquine has become the symbol of a struggle between practising physicians and methodologists [1], and the Western world against the rest of the world [2]. This leads to great confusion in the literature between, on the one hand, the advocates of an empirical approach based on the sensitivity of bacteria, viruses or parasites to anti-infectious agents in vitro and the rational use of these anti-infectious agents in patients, and, on the other hand, the analysts who, taking up the various studies, are more specifically interested in the form of the studies to determine the existence of biases. Recently, hydroxychloroquine, from my point of view, became a paradigm of such conflict. For example, when testing hydroxychloroquine, exclusion of patients without confirmed diagnosis is for us a major issue. In contrast, several studies do not consider this parameter to be essential [3,4], including a large study testing the efficacy of hydroxychloroquine prophylaxis [5]. This inclusion criterion is mandatory for me. In addition, there are considerable differences in dosing regimens as there is no standard dosing regimen: the Recovery Trial gave a theoretically toxic dosage at baseline (2.4 g), others use 200 mg daily, while we prescribe, in my institute, 600 mg daily, as in Q fever or Whipple's disease [6]. Also, it is important to compare the duration of treatment. Entering hospital data with a hydroxychloroquine yes/no answer does not tell much about the DOI of original article: https://doi.org/10.1016/j.cmi.2020.08.022. dite ranne e Infection, Aix Marseille Universite , Marseille, France. * IHU Me E-mail address: didier.raoult@gmail.com. treatment. These are major problems with the inclusion criteria in big data studies. The stage of the disease at which treatment is given is critical. Two reviews explain that there are four stages in the disease [6,7]. There is a first virological stage, when the antiviral drugs can be effective, a second viro-immunological stage, when the immunological reaction aggravates the patient's condition and is associated with abnormal coagulation phenomena including antiphospholipid antibodies [8], a third stage that is exclusively or almost exclusively immune, also called the cytokine storm, and finally a fourth stage, resulting from multiple pulmonary and visceral injuries. Thus, each stage probably corresponds to different therapeutic strategies. Finally, evaluation of the therapeutic efficiency in all infections of the lung is usually performed after 3 days of treatment. Mixing patients of different stages, with different doses and durations of treatment, may result in a false result known as “Simpson's paradox” [9]: studying separate groups of patients from the same study may result in opposite conclusions. Some French studies exclude patients who are treated while they have already been..
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