What meta-analysis features are available in Stata?

Stata does not have a meta-analysis command. Stata users, however, have developed an excellent suite of commands for performing meta-analyses.

1. meta

meta was the first Stata meta-analysis command. It requires the user to supply the treatment effect estimate and its standard error for each study. It uses inverse-variance weighting to derive fixed- and random-effects summary estimates of the treatment effect estimate.

The meta command has not been updated since 1998 and uses Stata 7 graphics. It is essentially redundant except that some other Stata meta-analysis commands require it to be installed.

To find out more, type the following in Stata:

        . findit meta

2. metan

The original version of the metan command used as input the cell frequencies from the 2 × 2 table for each study (for binary outcomes) or the mean and standard deviation in each group (for numerical outcomes). It provides a comprehensive range of methods for meta-analysis, including inverse-variance–weighted meta-analysis, and creates new variables containing the treatment effect estimate and its standard error for each study. These variables can then be used as input to several other Stata meta-analysis commands.

All the meta-analysis calculations available in metan are based on standard methods, an overview of which may be found in chapter 15 of Deeks, Altman, and Bradburn (2001).

metan has been updated on several occasions. Because it now allows the user to supply the treatment effect estimate and its standard error for each study, the command now has (almost) all the functionality of meta. Somewhat confusingly, the release of metan that added this facility was made available on the SSC archive in a package called metaaggr (meta-analysis of aggregate data). This may have meant that some users continued with older versions of the command.

Other important new facilities added include the by() option to conduct meta-analyses in subgroups and the recent update to Stata 9 graphics. The version of the metan command that used Stata 7 graphics has been renamed metan7 and is downloaded as part of the metan package currently available on the SSC archive.

The most recent help file for metan provides several clickable examples of using the command.

To find out more, type the following in Stata:

        . ssc describe metan

To install the package, type the following in Stata:

        . ssc install metan

3. metareg

metareg does meta-regression. It was released in 1998, with a major update made available on the SSC archive in 2004. It requires the user to input the treatment effect estimate and its standard error for each study.

To find out more, type the following in Stata:

        . ssc describe metareg

To install the package, type the following in Stata:

        . ssc install metareg

4. metabias

metabias reports results of the Begg and Mazumdar (1994) and Egger et al. (1997) tests for funnel plot asymmetry. It also produces funnel plots and Galbraith plots, but these use Stata 7 graphics. It was released in 1997 and updates have been made available on the SSC archive on several occasions since then. It requires the user to input the treatment effect estimate and its standard error for each study.

To find out more, type the following in Stata:

        . ssc describe metabias

To install the package, type the following in Stata:

        . ssc install metabias

5. metafunnel

metafunnel displays funnel plots. It was released in 2004 and uses Stata 8 graphics. It requires the user to input the treatment effect estimate and its standard error for each study.

To find out more, type the following in Stata:

        . ssc describe metafunnel

To install the package, type the following in Stata:

        . ssc install metafunnel

6. metatrim

metatrim implements the “trim and fill” method to adjust for publication bias in funnel plots. The most recent release was in 2003. It requires the user to input the treatment effect estimate and its standard error for each study.

To find out more, type the following in Stata:

        . ssc describe metatrim

To install the package, type the following in Stata:

        . ssc install metatrim

7. metacum

metacum performs cumulative meta-analyses and graphs the results. It does this by using repeat calls to the meta command. It was released in 1998 and has not been updated. It uses Stata 7 graphics.

To find out more, type the following in Stata:

        . findit metacum

8. metap

metap combines p-values by using Fisher’s method, Edgington’s additive method, or Edgington’s normal curve method. It was released in 1999 as a version 6 command (no graphics) and last updated in 2000. It requires the user to input a p-value for each study.

To find out more, type the following in Stata:

        . findit metap

9. metannt

metannt computes the number needed to treat (NNT) and the number of events avoided (or added) per 1,000. It is designed to aid interpretation of meta-analyses of binary data by presenting the effect sizes in absolute terms. It was released in 2003 as a version 7 command (no graphics) and has not been updated. It requires the user to input design parameters and uses metan to calculate needed statistics.

To find out more, type the following in Stata:

        . findit metannt

10. metainf

metainf investigates the influence of one study on the overall meta-analysis estimate and shows graphically the results when the meta-analysis estimates are computed, omitting one study in each turn. This command makes repeated calls to the meta command for its analyses. It was released in 1998 as a version 6 command using version 6 graphics and was last updated in 2000. It requires the user to provide input in the form needed by meta.

To find out more, type the following in Stata:

        . findit metainf

11. metaninf

metaninf is a port of the metainf command to use metan as its analysis engine rather than meta. It was released in 2001 as a version 6 command using version 6 graphics and was last updated in 2004. It requires the user to provide input in the form needed by metan.

To find out more, type the following in Stata:

        . ssc describe metaninf

To install the package, type the following in Stata:

        . ssc install metaninf

12. galbr, galbr8, and rgalbr

galbr, galbr8, and rgalbr provide a graphical display giving a visual impression of the amount of heterogeneity in a meta-analysis. The galbr command was released in 1997 as a version 6 command using version 6 graphics and was last updated in 2000. The galbr8 command was a port to version 8 with version 8 graphics and was released in 2005. The rgalbr command uses a radial graphical display. It is a version 8 command and was released in 2005 only in test form via Statalist. Each requires the user to provide input in the form needed by meta.

To find out more, type the following in Stata:

        . findit galbr

13. labbe

labbe draws a L’Abbe plot for event data (proportion of successes in the two groups). It is available via the metaaggr package as a version 7 command that uses version 6 graphics. It requires the user to provide input in the form needed by metan.

To find out more, type the following in Stata:

        . findit labbe

14. metagraph

metagraph draws a forest plot by using Stata 8 graphics. It can be used directly after a meta command or the user can input the combined estimate and confidence interval. It requires the user to provide input in the form needed by meta. The command was released in 2005 and last updated in 2006.

To find out more, type the following in Stata:

        . ssc describe metagraph

To install the package, type the following in Stata:

        . ssc install metagraph

15. heterogi

heterogi is an immediate command that provides the statistics H and I² to quantify heterogeneity in a meta-analysis. It is a version 8 command released in 2005. It requires the user to input the Q statistic and its df, as reported by meta or metan. (The I² statistic is now directly available in metan.)

To find out more, type the following in Stata:

        . ssc describe heterogi

To install the package, type the following in Stata:

        . ssc install heterogi

16. funnel and funnel2

funnel and funnel2 were released with metan to draw funnel plots.

To find out more, type the following in Stata:

To find out more, type the following in Stata:

        . ssc describe metan

To install the package, type the following in Stata:

        . ssc install metan

17. meta_lr

meta_lr graphs positive and negative likelihood ratios in diagnostic tests. It can do stratified meta-analysis of individual estimates. The user must provide the effect estimates (log positive likelihood ratio and log negative likelihood ratio) and their standard errors. Commands meta and metareg are used for internal calculations. This is a version 8 command released in 2004.

To find out more, type the following in Stata:

        . ssc describe meta_lr

To install the package, type the following in Stata:

        . ssc install meta_lr

18. metaparm

metaparm performs meta-analyses and calculates confidence intervals and p-values for differences or ratios between parameters for different subpopulations for data stored in the parmest format.

To find out more, type the following in Stata:

        . ssc describe metaparm

To install the package, type the following in Stata:

        . ssc install metaparm

19. glst

glst calculates a log-linear dose–response regression model using generalized least squares for trend estimation of single or multiple summarized dose–response epidemiological studies. Output from this command may be useful in deriving summary effects and their standard errors for inclusion in meta-analyses of such studies.

To find out more, type the following in Stata:

        . ssc describe glst

To install the package, type the following in Stata:

        . ssc install glst

References

Begg, C. B., and M. Mazumdar. 1994.

Operating characteristics of a rank correlation test for publication bias. Biometrics 50: 1088–1101.

Deeks, J. J., D. G. Altman, and M. J. Bradburn. 2001.

Statistical methods for examining heterogeneity and combining results from several studies in meta-analysis. In Systematic Reviews in Health Care: Meta-Analysis in Context, 2nd Edition, ed. M. Egger, G. D. Smith, and D. G. Altman. London: BMJ.

Egger, M., G. D. Smith, M. Schneider, and C. Minder. 1997.

Bias in meta-analysis detected by a simple, graphical test. British Medical Journal 315: 629–634.