2.3. Selection of data extraction
Eligible studies were screened in their entirety and developed a data extraction form, and the information including authors and year of publication, publication type, sample size, gender, type of treatment was recorded in an Excel spreadsheet. We pre-tested it on five studies and subsequently adapted the final version. If the inclusion criteria were met, the full-text of each study was coded by the first authors (J.Y. and H.J) using this template, difficulties in deciding the inclusion status of studies were discussed between two authors and resolved by consensus.
If there was any evidence for the use of the same sample in different publications, authors were contacted for clarification25. If it was confirmed that two studies were based on the same data, we chose the study that reported the most comprehensive results. Study authors were also contacted to request additional information if a study was eligible based on the inclusion criteria, but not all required data could be retrieved from the full-text. Overall, we adopted a conservative procedure in that we excluded studies for which ambiguity about the primary data source could not be resolved or if the information necessary for effect size calculation could not be acquired26. If studies reported on the results of two independent treatment subgroups, effect sizes for each subgroup were included in the meta-analysis and treated as a separate study to enable moderator analyses for the type of treatment.
2.4. Heterogeneity, sensitivity, and publication bias
Based on the Cochrane Handbook Version 6.1.0, 2020, heterogeneity was assessed using Cochrane’s Q-statistics to estimated the standard deviation of the true effect sizes and theI2 -Index27. A significant Q-test indicates that effect sizes of primary studies do not belong to the same distribution of effect sizes. When performance qualification statistics ≥0.05 (P ≥0.05), was considered no significant heterogeneity among the included studies25.I 2 index is used to the estimated amount of variability in the true effect sizes, and the proportion of observed variability that can be explained by true heterogeneity. Values of 25%, 50%, and 75% for the I2 -index indicate low, moderate, and high degrees of heterogeneity, respectively. To further explain the heterogeneity of our main results.
The ”Leave-one-out” method is used in sensitivity analyses to check for outliers that potentially influence the results of the meta-analysis disproportionately25. All analyses were performed repeatedly with each study removed once to detect whether overall results depend on a single study.
Publication bias means that statistically significant results are more likely to be published, while statistically insignificant results are less likely to be published. Therefore, these studies with no significant significance could be more likely to remain in the ”file drawer”28. Publication bias was assessed by three methods. Funnel plots illustrate the effect sizes of primary studies as a function of study precision, operationalized by the standard errors of studies. Asymmetry in plots can indicate publication bias29. Egger’s regression test yields a statistical verification of funnel plot asymmetry. If any bias could be assumed based on these analyses, we planned to apply the trim-and-fill procedure to estimate the unbiased overall effect30.