Keeping Meta-Analyses Hygienic During the COVID-19 Pandemic

• Autores: Boudesseul, Jordane, Zerhouni, Oulmann, Harbert, Allie, Rubinos, Clio
• Formato: artículo
• Fecha de Publicación: 2021
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/19585
• Enlace del recurso: https://hdl.handle.net/20.500.12724/19585; https://doi.org/10.3389/fpubh.2021.722458
• Nivel de acceso: acceso abierto
• Materia: Meta-analysis; Hydroxychloroquine; Metaanálisis; Hidroxicloroquina; COVID-19; https://purl.org/pe-repo/ocde/ford#5.01.00

Despite the massive distribution of different vaccines globally, the current pandemic has revealed the crucial need for an efficient treatment against COVID-19. Meta-analyses have historically been extremely useful to determine treatment efficacy but recent debates about the use of hydroxychloroquine for COVID-19 patients resulted in contradictory meta-analytical results. Different factors during the COVID-19 pandemic have impacted key features of conducting a good meta-analysis. Some meta-analyses did not evaluate or treat substantial heterogeneity (I2 > 75%); others did not include additional analysis for publication bias; none checked for evidence of p–hacking in the primary studies nor used recent methods (i.e., p-curve or p-uniform) to estimate the average population-size effect. These inconsistencies may contribute to contradictory results in the research evaluating COVID-19 treatments. A prominent example of this is the use of hydroxychloroquine, where some studies reported a large positive effect, whereas others indicated no significant effect or even increased mortality when hydroxychloroquine was used with the antibiotic azithromycin. In this paper, we first recall the benefits and fundamental steps of good quality meta-analysis. Then, we examine various meta-analyses on hydroxychloroquine treatments for COVID-19 patients that led to contradictory results and causes for this discrepancy. We then highlight recent tools that contribute to evaluate publication bias and p-hacking (i.e., p-curve, p-uniform) and conclude by making technical recommendations that meta-analyses should follow even during extreme global events such as a pandemic.