robustSE {MAd}

Robust standard error

Description

When the correlation between dependent effect sizes are unknown, one approach is to conduct the meta-analysis by assuming that the effect sizes are independent. Then, Hedges et al. (2010) robust standard error procedure can be calculated to adjust for dependence.

Usage

robustSE(model, cluster=NULL, CI=.95, digits=3)

Arguments

model

omnibus or moderator model object fitted from mareg() function.

cluster

Name of variable where the dependencies are present. This will typically be the variable for study id where length(unique(study_id))>1.

CI

Confidence interval. Defaults to .95.

digits

Number of digits to output. Defaults to 3.

Value

estimate

Meta-regression coefficient estimate.

se

Adjusted Standard error of the estimate coefficient.

t

t-value.

ci.l

Adjusted Lower 95% confidence interval.

ci.u

Adjusted Upper 95% confidence interval.

p

p-value.

Author(s)

Mike Cheung with modifications by AC Del Re

References

Hedges, L. V., Tipton, E., & Johnson, M. C. (2010). Robust variance estimation in meta-regression with dependent effect size estimates. Research Synthesis Methods, 1(1), 39-65. doi:10.1002/jrsm.5

Cheung, M.W.L. (2012). metaSEM: An R package for meta-analysis using structural equation modeling. Manuscript submitted for publication.

See Also

mareg,

Examples

# install metafor
# install.packages('metafor', dependencies = TRUE)

# Sample data
id<-c(1:20)
n.1<-c(10,20,13,22,28,12,12,36,19,12,36,75,33,121,37,14,40,16,14,20)
n.2 <- c(11,22,10,20,25,12,12,36,19,11,34,75,33,120,37,14,40,16,10,21)
g <- c(.68,.56,.23,.64,.49,-.04,1.49,1.33,.58,1.18,-.11,1.27,.26,.40,.49,
.51,.40,.34,.42,1.16)
var.g <- c(.08,.06,.03,.04,.09,.04,.009,.033,.0058,.018,.011,.027,.026,.0040,
.049,.0051,.040,.034,.0042,.016)
mod<-factor(c(rep(c(1,1,2,3),5)))
mods2<-c(rep(1:5,4))
df<-data.frame(id, n.1,n.2, g, var.g,mod, mods2)


# Examples

# Adjusted SE
robustSE(mareg(g~ mod + mods2, var = var.g, method = "REML", data = df))