What meta-analysis features are available in Stata?
User-written packages for meta-analysis in Stata
Jonathan A. C. Sterne, University of Bristol
Ross J. Harris, University of Bristol
Roger M. Harbord, University of Bristol
Thomas J. Steichen, RJRT
January 2007; updated July 2011
Stata does not have a meta-analysis command. Stata users, however, have
developed an excellent suite of commands for performing meta-analyses.
In 2009, Stata published Meta-Analysis in Stata: An Updated Collection
from the Stata Journal, which brought together all the Stata
Journal articles about meta-analysis. This book is available for
We have created a command to download all user-written commands discussed in
the body of the book. For instructions on obtaining this command, see
The following meta-analysis commands are all described in Meta-Analysis in
Stata: An Updated Collection from the Stata Journal.
metan is the main Stata meta-analysis command. Its latest version
allows the user to input the cell
frequencies from the 2 × 2 table for each study (for binary
outcomes), the mean and standard deviation in each group (for numerical
outcomes), or the effect estimate and standard error from each study. 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 other
Stata meta-analysis commands. Meta-analyses may be conducted in subgroups
by using the by() option.
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).
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.
labbe draws a L’Abbe plot for event data (proportions of
successes in the two groups).
metacum performs cumulative meta-analyses and graphs the results.
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 was last
updated in 2000. It requires the user to input a p-value for each
metareg does meta-regression. It was first released in 1998 and has been updated to take account of improvements in Stata estimation facilities and recent methodological developments. It requires the user to
input the treatment effect estimate and its standard error for each study.
metafunnel plots 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.
confunnel plots contour-enhanced funnel plots. The command has been designed to be flexible, allowing the user to add extra features to the funnel plot.
metabias provides statistical tests for funnel plot asymmetry. It was
first released in 1997, but it has been updated to provide recently proposed
tests that maintain better control of the false-positive rate than those
available in the original command.
metatrim implements the “trim and fill” method to adjust
for publication bias in funnel plots. It requires the user to input the
treatment effect estimate and its standard error for each study.
metandi facilitates the fitting of hierarchical logistic regression
models for meta-analysis of diagnostic test accuracy studies.
metandiplot produces a graph of the model fit by metandi,
which must be the last estimation-class command executed.
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.
metamiss performs meta-analysis with binary outcomes when some or all studies have missing data.
mvmeta performs maximum likelihood, restricted maximum likelihood, or
method-of-moments estimation of random-effects multivariate meta-analysis
models. mvmeta_make facilitates the preparation of summary datasets
from more detailed data.
The following commands are documented in the Appendix:
metannt is intended to aid interpretation of meta-analyses of binary
data by presenting intervention effect sizes in absolute terms, as the
number needed to treat (NNT) and the number of events avoided (or added) per
1,000. The user inputs design parameters, and metannt uses the
metan command to calculate the required statistics. This command is
available as part of the metan package.
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 install the package, type ssc install metaninf in Stata.
midas provides statistical and graphical routines for undertaking
meta-analysis of diagnostic test performance in Stata.
To install the package, type ssc install midas in Stata.
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 install the package, type ssc install meta_lr in Stata.
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 install the package, type ssc install metaparm in Stata.
The following command appeared in the Stata Journal after the
publication of Meta-Analysis in Stata: An Updated Collection from the
metaan performs meta-analysis on effect estimates and standard
errors. Included are profile likelihood and permutation estimation, two
algorithms not available in metan.
- 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.
Davey Smith, and D. G. Altman. London: BMJ.