hetmeta: Deriving Measures Of Heterogeneity
In hetmeta: Heterogeneity Measures in Meta-Analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

The "hetmeta" implements the most common measures of heterogenity in meta-analysis.

1	hetmeta(model)

model

an object of class "rma.uni".

The "hetmeta" function calculates estimates for several heterogeneity measures in meta-analysis based on a meta-analytic model of class rma.uni (see metafor-package for more details).

Specifically, the measures derived in the function are the $R_b$, $I^2$, and $R_I$. To complement those measures, the Dersimonian-Laird $Q$ test is presented, together with the coefficient of variation of the pooled estimate $CV_b$, coefficient of variation of the within-study variances, and the typical within-variance terms as defined in the $I^2$ and $R_I$. See references for more details.

The hetmeta function returns an object of class "hetmeta" as described in hetmetaObject.

Alessio Crippa, alessio.crippa@ki.se

Crippa A, Khudyakov P, Wang M, Orsini N, Spiegelman D. A new measure of between-studies heterogeneity in meta-analysis. 2016. Stat. Med. In Press.

Takkouche B, Khudyakov P, Costa-Bouzas J, Spiegelman D. Confidence Intervals for Heterogeneity Measures in Meta-analysis. Am. J. Epidemiol. 2013:kwt060.

Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat. Med. 2002; 21(11):1539-1558.

Takkouche B, Cadarso-Suarez C, Spiegelman D. Evaluation of old and new tests of heterogeneity in epidemiologic meta-analysis. Am. J. Epi- demiol. 1999; 150(2):206-215.

hetmeta-package, metafor

## load data
dat <- get(data(dat.gibson2002))

## random-effects model analysis of the standardized mean differences
dat <- escalc(measure = "SMD", m1i = m1i, sd1i = sd1i, n1i = n1i, m2i = m2i,
              sd2i = sd2i, n2i = n2i, data = dat)
res <- rma(yi, vi, data = dat, method = "REML")

## heterogeneity measures
hetmeta(res)


## load BCG vaccine data
data(dat.bcg)

## random-effects model of log relative risks
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)
res <- rma(yi, vi, data=dat)

## heterogeneity measures
hetmeta(res)