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Meta-Analyst: software for meta-analysis of binary, continuous and diagnostic data

Byron C Wallace12*, Christopher H Schmid13, Joseph Lau1 and Thomas A Trikalinos1

Author Affiliations

1 Center for Clinical Evidence Synthesis, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, 02111, USA

2 Department of Computer Science, Tufts University, Medford, MA, 02155, USA

3 Biostatistics Research Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, 02111, USA

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BMC Medical Research Methodology 2009, 9:80  doi:10.1186/1471-2288-9-80

Published: 4 December 2009

Abstract

Background

Meta-analysis is increasingly used as a key source of evidence synthesis to inform clinical practice. The theory and statistical foundations of meta-analysis continually evolve, providing solutions to many new and challenging problems. In practice, most meta-analyses are performed in general statistical packages or dedicated meta-analysis programs.

Results

Herein, we introduce Meta-Analyst, a novel, powerful, intuitive, and free meta-analysis program for the meta-analysis of a variety of problems. Meta-Analyst is implemented in C# atop of the Microsoft .NET framework, and features a graphical user interface. The software performs several meta-analysis and meta-regression models for binary and continuous outcomes, as well as analyses for diagnostic and prognostic test studies in the frequentist and Bayesian frameworks. Moreover, Meta-Analyst includes a flexible tool to edit and customize generated meta-analysis graphs (e.g., forest plots) and provides output in many formats (images, Adobe PDF, Microsoft Word-ready RTF). The software architecture employed allows for rapid changes to be made to either the Graphical User Interface (GUI) or to the analytic modules.

We verified the numerical precision of Meta-Analyst by comparing its output with that from standard meta-analysis routines in Stata over a large database of 11,803 meta-analyses of binary outcome data, and 6,881 meta-analyses of continuous outcome data from the Cochrane Library of Systematic Reviews. Results from analyses of diagnostic and prognostic test studies have been verified in a limited number of meta-analyses versus MetaDisc and MetaTest. Bayesian statistical analyses use the OpenBUGS calculation engine (and are thus as accurate as the standalone OpenBUGS software).

Conclusion

We have developed and validated a new program for conducting meta-analyses that combines the advantages of existing software for this task.