Meta-analysis increases the statistical power to detect effects for which individual studies may be underpowered. A central component of many systematic reviews is meta-analysis, the quantitative synthesis of information across methodologically and epidemiologically similar studies that address the same research question. Systematic reviews of randomized controlled trials or epidemiological studies have emerged as a key source of evidence across medical disciplines. We have developed and validated a new program for conducting meta-analyses that combines the advantages of existing software for this task. Bayesian statistical analyses use the OpenBUGS calculation engine (and are thus as accurate as the standalone OpenBUGS software).
Results from analyses of diagnostic and prognostic test studies have been verified in a limited number of meta-analyses versus MetaDisc and MetaTest. 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. The software architecture employed allows for rapid changes to be made to either the Graphical User Interface (GUI) or to the analytic modules. 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 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. NET framework, and features a graphical user interface. Meta-Analyst is implemented in C# atop of the Microsoft. Herein, we introduce Meta-Analyst, a novel, powerful, intuitive, and free meta-analysis program for the meta-analysis of a variety of problems. In practice, most meta-analyses are performed in general statistical packages or dedicated meta-analysis programs. The theory and statistical foundations of meta-analysis continually evolve, providing solutions to many new and challenging problems. Meta-analysis is increasingly used as a key source of evidence synthesis to inform clinical practice.
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