Description Usage Arguments Value Author(s) References Examples
The function r.vcov
computes effect sizes and variance-covariance matrices between effect sizes of the same of different types. Effect sizes include mean difference (MD), standardized mean difference (SMD), log odds ratio (logOR), log risk ratio (logRR), and risk difference (RD). Formulas are in Table I of Wei et al.'s paper (2013).
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d |
A N x p matrix or data frame with mean differences (MD) and/or 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. |
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 |
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 |
type |
A p-dimensional vector indicating types of effect sizes. |
name |
A p-dimensional vector storing names for the effect sizes. |
na.impute |
Missing values in |
ef |
A N x p data frame whose columns are computed effect sizes according to the input argument |
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
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 a multivariate meta-analysis
# Choose variable SBP, DBP, DD, D with effect sizes "MD","MD","RD","lgOR"
#########################################################################
data(Geeganage2010)
## set the correlation coefficients list r
r12 <- 0.71
r13 <- 0.5
r14 <- 0.25
r23 <- 0.6
r24 <- 0.16
r34 <- 0.16
r <- vecTosm(c(r12, r13, r14, r23, r24, r34))
diag(r) <- 1
mix.r <- lapply(1:nrow(Geeganage2010), function(i){r})
attach(Geeganage2010)
## compute variance co-variances
computvcov <- mix.vcov(type = c("MD", "MD", "RD", "lgOR"),
d = cbind(MD_SBP, MD_DBP, NA, NA),
sdt = cbind(sdt_SBP, sdt_DBP, NA, NA),
sdc = cbind(sdc_SBP, sdc_DBP, NA, NA),
nt = cbind(nt_SBP, nt_DBP, nt_DD, nt_D),
nc = cbind(nc_SBP, nc_DBP, nc_DD, nc_D),
st = cbind(NA, NA, st_DD, st_D),
sc = cbind(NA, NA, sc_DD, sc_D),
r = mix.r,
name = c("MD.SBP", "MD.DBP", "RD.DD", "lgOR.D"))
# save different effect sizes in y
y <- computvcov$ef
head(y)
# save variances and covariances of all the effect sizes in a matrix S
S <- computvcov$matrix.vcov
S[1, ]
## fixed-effect model
MMA_FE <- summary(metafixed(y = y, Slist = computvcov$list.vcov))
#######################################################################
# Running random-effects model using package "mvmeta" or "metaSEM"
#######################################################################
# Restricted maximum likelihood (REML) estimator from the mvmeta package
# library(mvmeta)
# mvmeta_RE <- summary(mvmeta(cbind(MD.SBP, MD.DBP, RD.DD, lgOR.D) ~.,
# S = S, data = y, method = "reml"))
# mvmeta_RE
# maximum likelihood estimators from the metaSEM package
# library(metaSEM)
# metaSEM_RE <- summary(meta(y = y, v = S))
# metaSEM_RE
##############################################################
# Plotting the result:
##############################################################
obj <- MMA_FE
# obj <- mvmeta_RE
# obj <- metaSEM_RE
# pdf("CI.pdf", width = 4, height = 7)
plotCI(y = computvcov$ef, v = computvcov$list.vcov,
name.y = NULL, name.study = Geeganage2010$studyID,
y.all = obj$coefficients[,1],
y.all.se = obj$coefficients[,2],
hline = c(0, 0, 0, 1))
# dev.off()
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