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 risk 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 |
st |
A N x p matrix recording number of participants with event for all outcomes (dichotomous) in treatment group. |
sc |
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 |
ef |
A N x p data frame whose columns are computed risk differences. |
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 risk differences and 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 <- rd.vcov(nt = subset(Geeganage2010, select = c(nt_DD, nt_D)),
nc = subset(Geeganage2010, select = c(nc_DD, nc_D)),
st = subset(Geeganage2010, select = c(st_DD, st_D)),
sc = subset(Geeganage2010, select = c(sc_DD, sc_D)),
r = r.Gee)
# name computed relative risk as y
y <- computvcov$ef
colnames(y) <- c("rd.DD", "rd.D")
# name variance-covariance matrix of trnasformed z scores as covars
S <- computvcov$matrix.vcov
## fixed-effect model
MMA_FE <- summary(metafixed(y = y, Slist = computvcov$list.vcov))
#######################################################################
# Running random-effects model using package "mvmeta" or "metaSEM"
#######################################################################
#library(mvmeta)
#mvmeta_RE <- summary(mvmeta(cbind(rd.DD, rd.D),
# S = S, data = as.data.frame(y),
# method = "reml"))
#mvmeta_RE
# maximum likelihood estimators from the metaSEM package
# library(metaSEM)
# metaSEM_RE <- summary(meta(y = y, v = S))
# metaSEM_RE
##############################################################
# Plotting the result:
##############################################################
obj <- MMA_FE
# obj <- mvmeta_RE
# obj <- metaSEM_RE
# pdf("CI.pdf", width = 4, height = 7)
plotCI(y = computvcov$ef, v = computvcov$list.vcov,
name.y = c("rd.DD", "rd.D"),
name.study = Geeganage2010$studyID,
y.all = obj$coefficients[,1],
y.all.se = obj$coefficients[,2])
# dev.off()
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