Data synthesis and statistical analysis
The statistical analysis was performed with the statistical software open source R program (Version 3.41). Categorical outcomes were estimated by odds ratio (OR) with 95% CI. If “1” falls into the CI, the outcome would be considered “not statistically significant”. The mean difference (MD = mean of primary IOL – mean of primary aphakia) with a 95% confidence interval (CI) was adopted for continuous outcomes. If “0” falls into the CI, the outcome would be considered “not statistically significant”.
Heterogeneity across studies was tested with Q test and I2 statistic. If there was no heterogeneity across these studies (I2<50% ), the Mantel–Haenszel fixed-effect model would be used. I2 above 50% and the P-value below 0.1 would constitute a significant heterogeneity among these studies, and possible reasons would be explored by reviewing the included studies. Three approaches were used to detect the source of heterogeneity in this meta-analysis: sensitive analysis, subgroup analysis and meta regression. Through sensitivity analysis, we could determine whether the heterogeneity would decrease following exclusion of each study one by one. Subgroup analysis and meta regression would be performed according to the clinical characteristics of the included studies. Through these two approaches, we could identify the factor that induced heterogeneity in the meta-analysis. If sensitivity analysis and subgroup analysis did not decrease the heterogeneity, the random-effects model (DerSimonian-Laird) would be adopted to calculate pooled effect size.28,29.