*! version 2.01 01jan2022

Syntax

Subgroup (categorica) fixed- and random-effects
inverse-variance weighted meta-analysis.

maanova [if] [in] ,
var(varname)
w(varname)
se(varname)
[_options_]

where effectsize is the effect size variable and
catvar is a categorical variable. The effect size can be any
effect size type, such as Cohen's d, Hedges' g, logged odds
ratio, logged risk ratio, logged logit, r or Fisher's Zr,
among others. It is critical that the effect size is in its analyzable
form. For example, the effect size can be a logged odds ratio but not
an odds ratio. One of the following must also be specified:

o var(varname): the variance of the effect size

o w(varname): the inverse variance weight of the effect size

o se(varname): the standard error of the effect size

The relationship among these is assumed to be w =
1/var = 1/se^2.

Options

Model Type
model(_string_)
model type; default is REML (restricted maximum likelihood); options
include FE (fixed effect), DL (Dersimonian & Laird), HE (Hedges'), HS
(Hunter & Schmidt), SJ (Sidik-Jonkman), SJIT (Sidik-Jonkman,
iterative), ML (maximum likelihood), REML (restricted maximum
likelihood), and EB (empirical bayes)

Print Options
print(_string_)
print options convert results for ease of interpretation; exp
exponentiates results, ivzr is the inverse Fisher's z transformation,
producing r, and prop converts logits back into proportions

Tau^2 Options tau_unique(_string_) specifies
whether a common (default) or subgroup specific tau^2 is
used. tau_unique(YES) will estimate a separate tau^2 for each
subgroup.

Description

maanova performs a subgroup or categorical moderator
meta-analysis under either a fixed-effect model (also called a
common-effect model) or a random-effects model. Several estimators for
the random effects variance component (tau^2) are available. The
command requires an effect size, its associated standard error,
variance, or inverse variance weight, and a categorical variable.

Meta-analytic regresson (aka, meta-regression) can be performed
with the mareg command (see mareg). For a basic
meta-analysis returning the overall mean effect size and associated
statistics, see masum command (see masum).

As of Stata version 16.0, Stata has a built-in command for
conducting meta-analysis. See meta.

Acknowledgments

maanova was written by David B. Wilson and is an updated
version of a command written as a companion to a book on meta-analysis
he co-authored with Mark Lipsey (Lipsey & Wilson, 2001). Portions of
this program are based on code from Wolfgang Viechtbauer's
metafor package for R.

References

Lipsey, M. W., & Wilson, D. B. (2001). Practical
meta-analysis. Sage.