Description Usage Arguments Value Author(s) References Examples
The function md.vcov
computes effect sizes and variance-covariance matrix for multivariate meta-analysis when the effect sizes of interest are all measured by mean difference. See mix.vcov
for effect sizes of the same or different types.
1 |
r |
A N-dimensional list of p x p correlation matrices for the p outcomes from the N studies. |
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 |
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 |
sdt |
A N x p matrix storing sample standard deviations for each outcome from treatment group. |
sdc |
A matrix defined in a similar way as |
list.vcov |
A N-dimensional list of p(p+1)/2 x p(p+1)/2 matrices of computed variance-covariance matrices. |
matrix.vcov |
A N x p(p+1)/2 matrix whose rows are computed variance-covariance vectors. |
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.
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# Example: Geeganage2010 data
# Preparing covariances for multivariate meta-analysis
######################################################
## 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 <- md.vcov(nt = subset(Geeganage2010, select = c(nt_SBP, nt_DBP)),
nc = subset(Geeganage2010, select = c(nc_SBP, nc_DBP)),
sdt = subset(Geeganage2010, select=c(sdt_SBP, sdt_DBP)),
sdc = subset(Geeganage2010, select=c(sdc_SBP, sdc_DBP)),
r = r.Gee)
# name variance-covariance matrix as S
S <- computvcov$matrix.vcov
## fixed-effect model
y <- as.data.frame(subset(Geeganage2010, select = c(MD_SBP, MD_DBP)))
MMA_FE <- summary(metafixed(y = y, Slist = computvcov$list.vcov))
MMA_FE
#######################################################################
# 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(MD_SBP, MD_DBP), S = S,
# data = subset(Geeganage2010, select = c(MD_SBP, MD_DBP)),
# 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
# plotCI(y = y, v = computvcov$list.vcov,
# name.y = c("MD_SBP", "MD_DBP"), name.study = Geeganage2010$studyID,
# y.all = obj$coefficients[,1],
# y.all.se = obj$coefficients[,2])
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