Description Usage Arguments Value Author(s) References Examples
The function lgOR.vcov
computes effect sizes and variance-covariance matrix for multivariate meta-analysis when the effect sizes of interest are all measured by standardized mean difference. See mix.vcov
for effect sizes of the same or different types.
1 |
nt |
A N x p matrix storing sample sizes in the treatment group reporting the p outcomes. |
nc |
A matrix defined in a similar way as |
d |
A N x p matrix or data frame with standard mean differences (SMD) from the N studies. |
r |
A N-dimensional list of p x p correlation matrices for the p outcomes from the N studies. |
n_rt |
A N-dimensional list of p x p matrices storing sample sizes in the treatment group reporting pairwise outcomes in the off-diagonal elements. |
n_rc |
A list defined in a similar way as |
name |
Names for the outcomes. |
ef |
A N x p data frame that transforms the input argument |
list.vcov |
A N-dimensional list of p(p+1)/2 x p(p+1)/2 variance-covariance matrices for Hedges's g (Wei and Higgins, 2013). |
matrix.vcov |
A N x p(p+1)/2 whose rows are computed variance-covariance vectors for Hedges's g (Wei and Higgins, 2013). |
list.dvcov |
A N-dimensional list of p(p+1)/2 x p(p+1)/2 variance-covariance matrices for SMD (Olkin and Gleser, 2009). |
matrix.dvcov |
A N x p(p+1)/2 matrix whose rows are computed variance-covariance vectors for SMD (Olkin and Gleser, 2009). |
Min Lu
Ahn, S., Lu, M., Lefevor, G.T., Fedewa, A. & Celimli, S. (2016). Application of meta-analysis in sport and exercise science. In N. Ntoumanis, & N. Myers (Eds.), An Introduction to Intermediate and Advanced Statistical Analyses for Sport and Exercise Scientists (pp.233-253). Hoboken, NJ: John Wiley and Sons, Ltd.
Wei, Y. & Higgins, J. (2013). Estimating within study covariances in multivariate meta-analysis with multiple outcomes. Statistics in Medicine, 32(7), 119-1205.
Olkin, I. & Gleser, L. (2009). Stochastically dependent effect sizes. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta-analysis (pp. 357-376). New York: Russel Sage Foundation.
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# Example: Geeganage2010 data
# Preparing covarianceS for multivariate meta-analysis
######################################################
data(Geeganage2010)
## set the correlation coefficients list r
r12 <- 0.71
r.Gee <- lapply(1:nrow(Geeganage2010), function(i){matrix(c(1, r12, r12, 1), 2, 2)})
computvcov <- smd.vcov(nt = subset(Geeganage2010, select = c(nt_SBP, nt_DBP)),
nc = subset(Geeganage2010, select = c(nc_SBP, nc_DBP)),
d = subset(Geeganage2010, select = c(SMD_SBP, SMD_DBP)), r = r.Gee,
name = c("SMD_SBP", "SMD_DBP"))
# name variance-covariance matrix as S
S <- computvcov$matrix.vcov
## fixed-effect model
y <- computvcov$ef
MMA_FE <- summary(metafixed(y = y, Slist = computvcov$list.vcov))
#######################################################################
# Running random-effects model using package "mvmeta" or "metaSEM"
#######################################################################
# Restricted maximum likelihood (REML) estimator from the mvmeta package
#library(mvmeta)
#mvmeta_RE <- summary(mvmeta(cbind(SMD_SBP, SMD_DBP),
# S = S,
# data = y,
# method = "reml"))
#mvmeta_RE
# maximum likelihood estimators from the metaSEM package
# library(metaSEM)
# metaSEM_RE <- summary(meta(y = y, v = S))
# metaSEM_RE
##############################################################
# Plotting the result:
##############################################################
obj <- MMA_FE
# obj <- mvmeta_RE
# obj <- metaSEM_RE
# pdf("CI.pdf", width = 4, height = 7)
plotCI(y = computvcov$ef, v = computvcov$list.vcov,
name.y = NULL, name.study = Geeganage2010$studyID,
y.all = obj$coefficients[,1],
y.all.se = obj$coefficients[,2])
# dev.off()
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