MetaAnalyst: powerful meta-analysis software

New in β 3.0:

The most exciting new feature is the ability to edit forest plots after they have been generated. For details, see the help. We have also introduced automated subgroup analyses. Other updates include the ability to paste data directly from Excel, improved data importing, custom labels (columns) for inclusion in forest plots, as well as myriad small bug fixes.

β 2.0 release

The biggest two updates to this version from the last are: 1) exhaustive testing of many of the available methods (more on this below) and 2) integration of the BUGS library for Bayesian meta-analyses. Additionally, here is a selected list of new features/updates added since the last (&beta 1) release:

Testing

Since the last release, we have completed a thorough testing of many of the statistical routines used in MetaAnalyst. In particular, we verified our output over roughly 12,000 continuous meta-analyses and 7,000 binary data meta-analsyes (using all metrics and methods) against Stata (using metan) for the 'simple' (i.e. non-Bayesian) routines. The following tables summarize the datatypes and models currently available in MetaAnalyst. Further, the Testing Level column indicates how thoroughly the respective methods have been tested. This is a somewhat subjective scale, but roughly:

Table 1: Binary data: Methods and testing levels (outcomes: OR, RD and RR)
Model Testing Level
Fixed (Peto, Inverse Variance, Mantel-Haenszel)
Γ
Random (DerSimonian-Laird)
Γ
Expectation Maximization
β
Bayes (Linear, Quadratic and 'None' control rates)
β



Table 2: Continuous data: Methods and testing levels (outcomes: MD, Hedges' g, Cohen's D, and Glass delta)
Model Testing Level
Fixed
Γ
Random (DerSimonian-Laird)
Γ
Expectation Maximization
β
Bayes (Linear and 'None' control rates)
β



Table 3: Diagnostic data
Model Testing Level
Fixed
Γ
Random (DerSimonian-Laird)
Γ
Expectation Maximization
Α
Fixed SROC
Α
Random SROC
β
Bivariate
Α
Bayes
Β