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Learn about meta-analysis.

Watch Meta-analysis in Stata.

Data setup and effect sizes

• Effect sizes for two-sample binary data
• Odds ratio
• Peto's odds ratio
• Risk ratio
• Risk difference
• Effect sizes for two-sample continuous data
• Hedges's g
• Cohen's d
• Glass's delta (two versions)
• Unstandardized mean difference
• Effect sizes for one-sample binary data (prevalence data)
  • Freeman–Tukey-transformed proportion
  • Logit-transformed proportion
  • Raw proportion
• Effect sizes for correlation data New
  • Fisher's \(z\)-transformed correlation
  • Raw correlation
• Generic (precomputed) effect sizes
• Transformed effect sizes such as correlations and efficacies
• Different methods for zero-cells adjustment with binary data
• Update declared meta-analysis settings at any time
• Describe declared meta-analysis settings

Meta-analysis models

• Common-effect model
• Inverse-variance method
• Mantel–Haenszel method
• Fixed-effects model
• Inverse-variance method
• Mantel–Haenszel method
• Random-effects model
• Iterative methods: REML, MLE, and empirical Bayes
• Noniterative methods: DerSimonian–Laird, Hedges, Sidik–Jonkman, and Hunter–Schmidt
• Knapp–Hartung standard-error adjustment
• Prediction intervals
• Sensitivity analysis: User-specified values for heterogeneity parameters tau2 and I2

Meta-analysis summary Updated

• Standard meta-analysis
• Forest plots
• Subgroup meta-analysis
• One grouping variable
• Multiple grouping variables
• Subgroup forest plots
• Cumulative meta-analysis
• Standard analysis
• Stratified analysis
• Cumulative forest plots
• Leave-one-out meta-analysis

Forest plots Updated

• Standard forest plot
• Custom forest plot
• Subgroup forest plot
• Cumulative forest plot
• Leave-one-out forest plot
• Cropped CI ranges
• Multiple overall effects

Heterogeneity

• Basic summary
• Forest plots
• L'Abbé plots for binary data
• Subgroup meta-analysis
• Meta-regression
• Bubble plots
• Galbraith plots

Meta-regression

• Continuous and categorical moderators
• Fixed-effects and random-effects regression
• Multiplicative and additive residual heterogeneity
• Knapp–Hartung standard-error adjustment
• Postestimation features
• Fitted values
• Residuals
• Random effects
• Standard errors of predicted quantities
• Bubble plots
• Other standard postestimation tools such as margins, contrasts, and more