Meta-Analysis in Stata: An Updated Collection from the Stata Journal
By: |
Jonathan A. C. Sterne (editor) |
| Publisher: |
Stata Press |
| Copyright: |
2009 |
| ISBN-13: |
978-1-59718-049-8 |
| Pages: |
259; paperback |
| Price: |
$39.00 |
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Comment from the Stata technical group
Stata has some of the best statistical tools available for doing
meta-analysis. The unusual thing about these tools is that none of them are
part of official Stata, so you will not find them in the Stata
documentation. They are all contributed and documented by researchers in
the field who also happen to be proficient Stata developers.
Meta-analysis allows researchers to combine results of several studies into
a unified analysis that provides an overall estimate of the effect of
interest and to quantify the uncertainty of that estimate. This collection
of articles from the Stata
Journal makes the work of 21 authors available in one collection.
Previously, you had to dig through many Stata Journal articles (and
older Stata Technical Bulletin inserts)
to find all the programs. No more! All the articles are now in one volume,
and the associated commands can be installed at one time.
This is not merely a retrospective collection. Editor Jonathan Sterne
convinced over half the authors to update their software and articles for
the collection, resulting in a much more cohesive volume. The programs have
a more unified syntax than in their original forms and, among the commands
that draw graphs, almost all now produce modern Stata graphs—they can
even be edited in the Graph Editor.
In his opening comments and the introductions to each section, Sterne
relates how the articles tie together and how they fit in the overall
literature of meta-analysis. He organizes the collection into four areas:
classic meta-analysis; meta-regression; graphical and analytic tools for
detecting bias; and recent advances such as meta-analysis for
dose–response curves, diagnostic accuracy, multivariate analyses, and
studies containing missing values. The collection addresses both common and
complex methods for conducting a meta-analysis, including implementations of
contemporary advances that will help keep the reader up to date.
The collection includes 16 articles and 15 new Stata commands for
meta-analysis. The articles cover topics ranging from standard and
cumulative meta-analysis and forest plots to contour-enhanced funnel plots
and nonparametric analysis of publication bias. In their articles, the
authors present conceptual overviews of the techniques, thorough
explanations, and detailed descriptions and syntax of new commands. They
also provide examples using real-world data. In short, this collection is a
complete introduction and reference for performing meta-analyses in Stata.
Table of contents
Install the software
1 Meta-analysis in Stata: metan, metacum, and metap
metan—a command for meta-analysis in Stata
M. J. Bradburn, J. J. Deeks, and D. G. Altman
metan: fixed- and random-effects meta-analysis
R. J. Harris, M. J. Bradburn, J. J. Deeks, R. M. Harbord, D. G. Altman, and J. A. C. Sterne
Cumulative meta-analysis
J. A. C. Sterne
Meta-analysis of p-values
A. Tobias
2 Meta-regression: metareg
Meta-regression in Stata
R. M. Harbord and J. P. T. Higgins
Meta-analysis regression
S. Sharp
3 Investigating bias in meta-analysis: metafunnel, confunnel,
metabias, and metatrim
Funnel plots in meta-analysis
J. A. C. Sterne and R. M. Harbord
Contour-enhanced funnel plots for meta-analysis
T. M. Palmer, J. L. Peters, A. J. Sutton, and S. G. Moreno
Updated tests for small-study effects in meta-analyses
R. M. Harbord, R. J. Harris, and J. A. C. Sterne
Tests for publication bias in meta-analysis
T. J. Steichen
Tests for publication bias in meta-analysis
T. J. Steichen, M. Egger, and J. A. C. Sterne
Nonparametric trim and fill analysis of publication bias in meta-analysis
T. J. Steichen
4 Advanced methods: metandi, glst, metamiss, and mvmeta
metandi: Meta-analysis of diagnostic accuracy using hierarchical logistic regression
R. M. Harbord and P. Whiting
Generalized least squares for trend estimation of summarized dose–response data
N. Orsini, R. Bellocco, and S. Greenland
Meta-analysis with missing data
I. R. White and J. P. T. Higgins
Multivariate random-effects meta-analysis
I. R. White
Appendix
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