**2.2. Statistical analysis**

Effect sizes are indices that measure the magnitude of the differences between two groups. For each comparison, individual RCT data for each outcome measure and combined measure were pooled to calculated standardized mean difference (SMD) effect size considering *P* < 0.05 significant level [42] using the Comprehensive Meta-Analysis ver.2.2.064, a software package developed by Biostat (http://www.meta-analysis.com/; Englewood, NJ 08631 USA).

To show substantial heterogeneity among studies, we report fixed-, random-, and mixedeffects meta-analysis [43]. In this continuum, random-effects meta-analysis takes into account the precision of discrete studies and the variation among studies and weights of each study accordingly.

We conducted two subgroup analyses to explore association of dietary cholesterol and duration of cholesterol intake with outcome measures. For each subgroup category, overall net change estimates were calculated using fixed-effects, random-effects, and mixed-effects models, and the heterogeneity of estimates was assessed. We conducted a meta-regression analysis to further examine the effects of cholesterol intake as an explanatory factor on outcome variables using random-effects meta-regression (unrestricted maximum likelihood (UREML)).

The heterogeneity of studies was quantified using chi-square test, *Q*, and *I*<sup>2</sup> statistics [44]. Begg's [45] and Egger's tests [46] were employed to identify publication bias.
