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

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

 

Forest plots 

Standard forest plot

Custom forest plot

Subgroup forest plot

Cumulative forest plot

Cropped CI ranges

 

 

Heterogeneity

Basic summary

Forest plots

L’Abbé plots for binary data

Subgroup meta-analysis

Meta-regression

Bubble 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 marginscontrasts, 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

 

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

Stratified funnel plots

Fully customizable

 

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