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).