2.5. | Data analysis
Effect sizes were indicated as MD and 95% confidence interval (CI) and P≤0.05 were considered as statistically significant for each parameter in this meta-analysis. Net changes in the explored parameters (change scores) were computed by subtracting the value at baseline from the one after intervention, in the treatment group, and in the control one. Standard Deviation (SDs) of the Weighted Mean Differences (WMD) were obtained as reported by Follmann, Elliott, Suh, and Cutler (1992): SD difference = square root [(SD pre-treatment)2+(SDpost-treatment)2 - (2× R × SDpre-treatment× SD post-treatment)], assuming a correlation coefficient (R) = 0.8 as it is a conservative estimate for an expected range of 0-1. If the trials did not reported means and (SDs) of outcome measures, we converted the available statistical data into means and (SDs) by suitable formula: SD = SEM × √n, being “n” the number of subjects in any group. If medians and inter-quartile range were reported, mean and SD values were computed by the method described by Hozo et al[39].
I² testing performed find the potential sources of between-study heterogeneity. Fixed effect model chosen for meta-analysis due to (\(I^{2}\) was below 50% with p-value <0.1)[40], and selected the random-effects model if (\(I^{2}\) was above 50%) [41]. All analyses were performed by STATA software (version 14.0). Potential publication bias was explored using Egger’s regression test (Egger’s test) (21).