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Combine results of multiple studies to estimate an overall effect. Use forest plots to visualize results. Evaluate study heterogeneity with subgroup analysis or meta-regression. Use funnel plots and formal tests to explore publication bias and small-study effects. Assess the impact of publication bias on results with trim-and-fill analysis. Perform cumulative meta-analysis. Use the meta suite of commands, or let the Control Panel interface guide you through your entire meta-analysis.

Learn about meta-analysis.

See what's new in meta-analysis.

Watch Meta-analysis in Stata.

Data setup and effect sizes

  • Effect sizes for binary data
    • Odds ratio
    • Peto's odds ratio
    • Risk ratio
    • Risk difference
  • Effect sizes for continuous data
    • Hedges's g
    • Cohen's d
    • Glass's delta (two versions)
    • Unstandardized mean difference
  • 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

  • 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 New

Forest plots

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


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


  • 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

Small-study effects

  • Funnel plots
  • Tests for small-study effects

Funnel plots

  • Standard funnel plots
  • Contour-enhanced funnel plots
  • Two-sided or one-sided significance contours
  • Multiple precision metrics for the y-axis
  • Stratified funnel plots
  • Fully customizable

Tests for funnel-plot asymmetry or small-study effects

  • Egger regression-based test
  • Harbord regression-based test
  • Peters regression-based test
  • Begg rank correlation test
  • Adjust for moderators to account for heterogeneity
  • Traditional and random-effects versions

Publication bias

  • Funnel plots
  • Tests for funnel-plot assymetry
  • Nonparametric trim-and-fill method
    • Three estimators for number of missing studies
    • Impute studies on the left or right side of the funnel plot
    • Nine estimation methods for the iteration stage
    • Nine estimation methods for the pooling stage
    • Choose the side of the funnel plot with missing studies
    • Standard and contour-enhanced funnel plot for the observed and imputed studies

Multivariate meta-regression New

  • Multivariate meta-analysis
  • Fixed-effects and random-effects multivariate meta-regression
  • Estimation methods: REML, MLE, Jackson--White--Riley
  • Multivariate heterogeneity statistics
  • Jackson--Riley standard-error adjustment
  • Between-study covariance structures
  • Sensitivity analysis
  • Missing values
  • Postestimation features
    • Fitted values
    • Residuals
    • Random effect
    • Standard errors of predicted quantities
    • Assess heterogenerity
    • Other standard postestimation tools such as margins, contrasts, and more

Control panel

  • Set up data and compute effect sizes
  • Update specific characteristics at any time
  • Summarize results in tables and produce forest plots
  • Perform subgroup analysis and cumulative meta-analysis
  • Perform meta-regression and pick from a variety of postestimation tools
  • Perform publication bias analysis
  • Perform multivariate meta-regression and pick from a variety of postestimation tools

Additional resources

See New in Stata 17 to learn about what was added in Stata 17.





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