#forest-plot

Conduct quantitative meta-analysis following best practices. Data preparation: 1. Extract effect sizes and standard errors from each study. 2. Code study characteristics (sample size, population, methodology quality). 3. Handle multiple effect sizes from same study (average, select one, or use robust variance estimation). Statistical analysis in R metafor package: 1. Fixed effects model: assumes one true effect size. 2. Random effects model: assumes distribution of true effects. 3. Test for heterogeneity using Q-statistic and I² (>75% = high heterogeneity). 4. Moderator analysis: meta-regression or subgroup analysis to explain heterogeneity. 5. Publication bias assessment: funnel plots, Egger's test, trim-and-fill method. Report: Forest plot showing individual study effects and pooled estimate with 95% CI. Address limitations and clinical significance of findings.