Meta-analysis New
Learn about meta-analysis .
Watch Meta-analysis in Stata .
Data setup and effect sizes
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Effect sizes for binary data
- Odds ratio
- Peto's odds ratio
- Risk ratio
- Risk difference
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Effect sizes for continuous data
- Hedges's g
- Cohen's d
- Glass's delta (two versions)
- Unstandardized mean difference
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Generic (precomputed) effect sizes
- Transformed effect sizes such as correlations and efficacies
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Different methods for zero-cells adjustment with binary data
- Update declared meta-analysis settings at any time
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Describe declared meta-analysis settings
Meta-analysis models
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Common-effect model
- Inverse-variance method
- Mantel–Haenszel method
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Fixed-effects model
- Inverse-variance method
- Mantel–Haenszel method
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Random-effects model
- Iterative methods: REML, MLE, and empirical Bayes
- Noniterative methods: DerSimonian–Laird, Hedges, Sidik–Jonkman, and Hunter–Schmidt
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Knapp–Hartung standard-error adjustment
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Prediction intervals
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Sensitivity analysis: User-specified values for heterogeneity parameters tau2 and I2
Meta-analysis summary
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Standard meta-analysis
- Forest plots
- Subgroup meta-analysis
- One grouping variable
- Multiple grouping variables
- Subgroup forest plots
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Cumulative meta-analysis
- Standard analysis
- Stratified analysis
- Cumulative forest plots
Forest plots
- Standard forest plot
- Custom forest plot
- Subgroup forest plot
- Cumulative forest plot
- Cropped CI ranges
- Multiple overall effects
;)
Heterogeneity
- Basic summary
- Forest plots
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L'Abbé plots for binary data
- Subgroup meta-analysis
- Meta-regression
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Bubble plots
Meta-regression
- Continuous and categorical moderators
- Fixed-effects and random-effects regression
- Multiplicative and additive residual heterogeneity
- Knapp–Hartung standard-error adjustment
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Postestimation features
- Fitted values
- Residuals
- Random effects
- Standard errors of predicted quantities
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Bubble plots
- Other standard postestimation tools such as margins, contrasts, and more
Small-study effects
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Funnel plots
- Tests for small-study effects
Funnel plots
- Standard funnel plots
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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
;)
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
;)
Additional resources
- Meta-Analysis Reference Manual , especially
- Meta-Analysis in Stata: An Updated Collection from the Stata Journal, Second Edition by Tom M. Palmer and Jonathan A. C. Sterne (editors)
Read a brief overview of meta-analysis .
See New in Stata 16 for more about what was added in Stata 16.
