*! version 2.0 05may2021

Syntax

Basic fixed- and random-effects inverse-variance weighted meta-analysis.

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

where effectsize is the effect size 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

Description

masum performs a 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 and its associated
standard error, variance, or inverse variance weight.

Meta-analytic regresson (aka, meta-regression) can be performed
with the mareg command (see mareg). Subgroup or
categorical moderator analysis (i.e., analog-to-the-ANOVA) can be
performed with the maanova command (see maanova).

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

Acknowledgments

masum 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.

Stored results

masum stores the following in r():

Scalars

r(mean) mean effect size

r(tau2) random effects variance component