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metan: fixed- and random-effects meta-analysis AgEcon
Harris, Ross J.; Bradburn, Michael J.; Deeks, Jonathan J.; Harbord, Roger M.; Altman, Douglas G.; Sterne, Jonathan A.C..
This article describes updates of the meta-analysis command metan and options that have been added since the command’s original publication (Bradburn, Deeks, and Altman, metan — an alternative meta-analysis command, Stata Technical Bulletin Reprints, vol. 8, pp. 86–100). These include version 9 graphics with flexible display options, the ability to meta-analyze precalculated effect estimates, and the ability to analyze subgroups by using the by() option. Changes to the output, saved variables, and saved results are also described.
Tipo: Article Palavras-chave: Metan; Meta-analysis; Forest plot; Research Methods/ Statistical Methods.
Ano: 2008 URL: http://purl.umn.edu/120926
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Meta–regression in Stata AgEcon
Harbord, Roger M.; Higgins, Julian P.T..
We present a revised version of the metareg command, which performs meta-analysis regression (meta-regression) on study-level summary data. The major revisions involve improvements to the estimation methods and the addition of an option to use a permutation test to estimate p-values, including an adjustment for multiple testing. We have also made additions to the output, added an option to produce a graph, and included support for the predict command. Stata 8.0 or above is required.
Tipo: Article Palavras-chave: Metareg; Meta-regression; Meta-analysis; Permutation test; Multiple testing; Research Methods/ Statistical Methods.
Ano: 2008 URL: http://purl.umn.edu/122617
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Funnel plots in meta-analysis AgEcon
Sterne, Jonathan A.C.; Harbord, Roger M..
Funnel plots are a visual tool for investigating publication and other bias in meta-analysis. They are simple scatterplots of the treatment effects estimated from individual studies (horizontal axis) against a measure of study size (vertical axis). The name “funnel plot” is based on the precision in the estimation of the underlying treatment effect increasing as the sample size of component studies increases. Therefore, in the absence of bias, results from small studies will scatter widely at the bottom of the graph, with the spread narrowing among larger studies. Publication bias (the association of publication probability with the statistical significance of study results) may lead to asymmetrical funnel plots. It is, however, important to realize that...
Tipo: Journal Article Palavras-chave: Metafunnel; Funnel plots; Meta-analysis; Publication bias; Small-study effects; Research Methods/ Statistical Methods.
Ano: 2004 URL: http://purl.umn.edu/116233
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