Data synthesis
Treatment effects were reported as relative risk (RR) for dichotomous variables (positive RT-PCR status, composite measure of disease progression, death, and any adverse events) and standardized mean difference (SMD) for continuous variables (serum biomarkers of inflammation) with 95% confidence intervals (CI). To calculate the RR, the number of events and individuals in each treatment group were extracted. To calculate SMD, means and standard deviations (SD) were obtained for each study group. If the means and SD were not directly reported in the publication, indirect methods of extracting estimates were used [8]. A negative effect size indicated that nitazoxanide decreased levels of inflammatory biomarkers in patients with COVID-19.
We used either a fixed or random-effects model to pool the results of individual studies depending on the presence of heterogeneity. Statistical heterogeneity was quantified by the I2index using the following interpretation: 0%, no between-study heterogeneity; <50%, low heterogeneity; 50–75%, moderate heterogeneity; > 75%, high heterogeneity [9]. In the case of heterogeneity, we used the random-effects model, otherwise, the fixed-effects model was used.
Although funnel plots may be useful tools in investigating small study effects in meta-analyses, they have limited power to detect such effects when there are few studies [10]. Therefore, because we had only a small number of included studies, we did not perform a funnel plot analysis. Forest plots were used to present the effect sizes and the 95% CI, and a 2-tailed p < 0.05 was used to determine significance. Analyses were conducted using Review Manager, version 5.3 (Cochrane IMS).