Title  Userwritten packages for metaanalysis in Stata  
Authors 
Jonathan A. C. Sterne, University of Bristol Ross J. Harris, University of Bristol Roger M. Harbord, University of Bristol Thomas J. Steichen, RJRT 

Date  January 2007; updated January 2016 
Stata does not have a metaanalysis command. Stata users, however, have developed an excellent suite of commands for performing metaanalyses.
In 2016, Stata published MetaAnalysis in Stata: An Updated Collection from the Stata Journal, Second Edition, which brought together all the Stata Journal articles about metaanalysis. This book is available for purchase at statapress.com/books/metaanalysisinstata/.
The following metaanalysis commands are all described in MetaAnalysis in Stata: An Updated Collection from the Stata Journal, Second Edition.
metan is the main Stata metaanalysis 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 metaanalysis, including inversevariance–weighted metaanalysis, 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 metaanalysis commands. Metaanalyses may be conducted in subgroups by using the by() option.
All the metaanalysis 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).
metaan performs metaanalysis on effect estimates and standard errors. Included are profile likelihood and permutation estimation, two algorithms not available in metan.
metacum performs cumulative metaanalyses and graphs the results.
metap combines pvalues 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 pvalue for each study.
metareg does metaregression. 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 contourenhanced 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 falsepositive 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.
extfunnel implements a new range of overlay augmentations to the funnel plot to assess the impact of a new study on an existing metaanalysis.
metandi facilitates the fitting of hierarchical logistic regression models for metaanalysis of diagnostic test accuracy studies. metandiplot produces a graph of the model fit by metandi, which must be the last estimationclass command executed.
mvmeta performs maximum likelihood, restricted maximum likelihood, or methodofmoments estimation of randomeffects multivariate metaanalysis models. mvmeta_make facilitates the preparation of summary datasets from more detailed data.
ipdforest is a postestimation command that uses the stored estimates of an xtmixed or xtmelogit command for multilevel linear or logistic regression, respectively.
ipdmetan performs twostage individual participant data metaanalysis using the inversevariance method.
indirect performs pairwise indirect treatment comparisons.
network setup imports data from a set of studies reporting count data (events, total number) or quantitative data (mean, standard deviation, total number) for two or more treatments.
network import imports a dataset already formatted for network metaanalysis.
network table tabulates network metaanalysis data.
network pattern shows which treatments are used in which studies.
network map draws a map of a network; that is, it shows which treatments are directly compared against which other treatments and roughly how much information is available for each treatment and for each treatment comparison.
network convert converts between the three formats described in the help file for network.
network query displays the current network settings.
network unset deletes the current network settings.
network meta defines a model to be fit: either the consistency model or the designbytreatment interaction inconsistency model.
network rank ranks treatments after a network metaanalysis has been fit.
network sidesplit fits the nodesplitting model of Dias et al. (2010).
network forest draws a forest plot of network metaanalysis data.
networkplot plots a network of interventions using nodes and edges.
netweight calculates all direct pairwise summary effect sizes with their variances, creates the design matrix, and estimates the percentage contribution of each direct comparison to the network summary estimates and in the entire network.
ifplot identifies all triangular and quadratic loops in a network of interventions and estimates the respective inconsistency factors and their uncertainties.
netfunnel plots a comparisonadjusted funnel plot for assessing smallstudy effects within a network of interventions.
intervalplot plots the estimated effect sizes and their uncertainties for all pairwise comparisons in a network metaanalysis.
netleague creates a "league table" showing in the offdiagonal cells the relative treatment effects for all possible pairwise comparisons estimated in a network metaanalysis.
sucra gives the surface under the cumulative ranking curves percentages and mean ranks, and produces rankograms (line plots of the probabilities versus ranks) and cumulative ranking plots (line plots of the cumulative probabilities versus ranks) for all treatments in a network of interventions.
mdsrank creates the squared matrix containing the pairwise relative effect sizes and plots the resulting values of the unique dimension for each treatment.
clusterank performs hierarchical cluster analysis to group the competing treatments into meaningful groups.
glst calculates a loglinear 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 metaanalyses of such studies.
metamiss performs metaanalysis with binary outcomes when some or all studies have missing data.
Describes how to fit fixed and randomeffects metaanalysis models using the sem and gsem commands, introduced in Stata 12 and 13 respectively, for structural equation modeling.
metacumbounds provides zvalues, pvalues, and LanDeMets bounds obtained from fixed or randomeffects metaanalysis. It plots the boundaries and zvalues through a process.
metasim simulates a specified number of new studies based on the estimates obtained from a preexisting metaanalysis.
metapow implements an approach to estimating the power of a newly simulated study generated by using the program metasim.
metapowplot estimates the power of an updated metaanalysis including a new study and plots each value against a range of sample sizes.
The following commands are documented in the Appendix:
metacurve models a response as a function of a continuous covariate, optionally adjusting for other variable(s) specified by adjust().
metannt is intended to aid interpretation of metaanalyses 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.
midas provides statistical and graphical routines for undertaking metaanalysis of diagnostic test performance in Stata.
meta_lr graphs positive and negative likelihood ratios in diagnostic tests. It can do stratified metaanalysis 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.
metaparm performs metaanalyses and calculates confidence intervals and pvalues for differences or ratios between parameters for different subpopulations for data stored in the parmest format.
metaeff is a preprocessing command for metaanalysis and a companion to metaan which calculates effect sizes and their standard errors.