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st: New version of -metareg- on SSC


From   Roger Harbord <roger.harbord@bristol.ac.uk>
To   Statalist <statalist@hsphsun2.harvard.edu>
Subject   st: New version of -metareg- on SSC
Date   Thu, 23 Sep 2004 22:43:29 +0100

A new version of the -metareg- package is available from SSC, thanks to Kit Baum. To describe and install the package, type -ssc describe metareg- from Web-aware Stata. It requires Nick Cox's -matvsort- command, so if you decide to install it and don't already have -matvsort- installed type -ssc install matvsort-.

-metareg- performs random-effects meta-regression (meta-analysis regression) on study-level summary (aggregate) data. My thanks to Stephen Sharp for giving his permission to revise his -metareg- program (version 1.06 published January 1998 in STB-42 as package sbe23) and to issue the new version under the same name. The major revisions are:

(1) an option to apply a Monte Carlo permutation test to obtain p-values, as proposed by Higgins & Thompson (2004).

(2) an improved algorithm for REML estimation of the between studies component of variance tau-squared. This the Stata maximum likelihood routines to maximize the REML log-likelihood directly.

In addition the new version is written as a Stata estimation command and so allows use of many of Stata's post-estimation commands. It also supports several extra -predict- options.

The new version of -metareg- continues to support the syntax of the previous version, although this is not documented in the help-file to avoid further complicating the syntax diagram and confusing new users. Previous do-files that called -metareg- should therefore continue to work, although the results may not be identical due to (2) above.

More detail will be given in a planned future Stata Journal article, but I would very much welcome comments and suggested improvements before I submit a final version to Stata Journal. (However I will be away from my email from 25 September until 11 October so please be patient if emailing me queries before then.)

For information on permutation tests in meta-regression, including interpretation of the results, see:

Higgins JPT, Thompson SG (2004). Controlling the risk of spurious findings from meta-regression. Statistics in Medicine 23:1663-1682.
http://www3.interscience.wiley.com/cgi-bin/abstract/108565626/ABSTRACT

My thanks to Rebecca Boehm whose posts to Statalist originally inspired me to revisit -metareg- and to Aijing Shang for useful comments and suggestions.

Roger.
----------------------------------------------------
Roger Harbord mailto:roger.harbord@bristol.ac.uk
Department of Social Medicine, University of Bristol

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