Analyses and measures of treatment effect
Mean changes and SD were computed for continuous variables for every
study to calculate effect size. As for studies with no reported SD of
the mean difference, following formula were used: SD change = square
root [(SD baseline 2 + SD final 2)
- (2 × 0.8 × SD baseline × SD final)]32. Standardized mean
changes were used for variables pooled on the different scales.
Between-study heterogeneity was assessed using the chi-squared (χ2) test
and quantified using the I2 statistic, which
represents the percentage of the total variation across studies that is
attributable to heterogeneity rather than to chance. Significant
heterogeneity was defined with a P-value of <0.05.
The random effects model was applied to compute the weighted mean
differences (WMDs) with 95% confidence intervals (CIs) for estimating
the overall effect. To evaluate whether the results could have been
affected distinctly by a single study, an influence or sensitivity
analysis was carried out33. Subgroup analysis
was also performed, based on the gender of participant (male, female or
both), baseline BMI (over 30 kg/m2 and 30
kg/m2 or lower), dose of soy protein (over 20 g/day
and 20 g/day or lower), duration of studies (over 12 weeks and 12 weeks
or lower), supplementation type (soy-based meal replacement, soy powder
or capsule supplement) and study design (randomized cross over or
parallel). Publication bias was assessed by Begg’s rank correlation test
and Egger’s regression asymmetry test. Funnel plots also depicted the
effect sizes (differences in means) against their corresponding standard
errors. Statistical analyses were performed using STATA 11.2 software
(StataCorp, College Station, Texas, USA).