Table 4 The statistical methods used in this meta-analysis and there explanation
Statistic means | Goals and Usages | Explanation |
|---|---|---|
Labbe plot | To evaluate heterogeneity between the included studies | In Labbe figure, if the points basically present as a linear distribution, it can be taken as an evidence of homogeneity. |
Cochran’s Q test | To evaluate heterogeneity between the included studies | Cochran’s Q test is an extension to the McNemar test for related samples that provides a method for testing for differences between three or more matched sets of frequencies or proportions. Heterogeneity was also considered significant if P < 0.05 using the Cochran’s Q test. |
I2 index test | To evaluate heterogeneity between the included studies | The I2 index measures the extent of true heterogeneity dividing the difference between the result of the Q test and its degrees of freedom (k – 1) by the Q value itself, and multiplied by 100. I2 values of 25%, 50% and 75% were used as evidence of low, moderate and high heterogeneity, respectively. |
Sensitivity analysis | To examine the stability of the pooled results | A sensitivity analysis was performed using the one-at-a-time method, which involved omitting one study at a time and repeating the meta-analysis. If the omission of one study significantly changed the result, it implied that the result was sensitive to the studies included. |
Contour-enhanced funnel plot | Publication bias test | Visual inspection of the Contour-enhanced funnel plots was used to assess potential publication bias. Asymmetry in the plots, which may be due to studies missing on the left-hand side of the plot that represents low statistical significance, suggested publication bias. If studies were missing in the high statistical significance areas (on the right-hand side of the plot), the funnel asymmetry was not considered to be due to publication bias |