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 " |
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ## 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
het_values <- hetmeta(res)
het_values
## confidence intervals for heterogeneity measures
confint(het_values)
## 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)
|
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