rd.vcov: Computing Variance-Covariance Matrices for Risk Differences

Description Usage Arguments Value Author(s) References Examples

View source: R/rd.vcov.R

Description

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.

Usage

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rd.vcov(r, nt, nc, st, sc, n_rt = NA, n_rc = NA)

Arguments

r

A N-dimensional list of p x p correlation matrices for the p outcomes from the N studies. r[[k]][i,j] is the correlation coefficient between outcome i and outcome j from study k.

nt

A N x p matrix storing sample sizes in the treatment group reporting the p outcomes. nt[i,j] is the sample size from study i reporting outcome j.

nc

A matrix defined in a similar way as nt for the control group.

st

A N x p matrix recording number of participants with event for all outcomes (dichotomous) in treatment group. st[i,j] reports number of participants with event for outcome j in treatment group for study i. If outcome j is not dichotomous, NA has to be imputed in column j.

sc

Defined in a similar way as st for the control group.

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_rt[[k]][i,j] is the sample size reporting both outcome i and outcome j from study k. Diagonal elements of these matrices are discarded. The default value is NA, which means that the smaller sample size reporting the corresponding two outcomes is imputed: i.e. n_rt[[k]][i,j]=min(nt[k,i],nt[k,j]).

n_rc

A list defined in a similar way as n_rt for the control group.

Value

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.

Author(s)

Min Lu

References

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.

Examples

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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()

metavcov documentation built on Oct. 25, 2021, 9:08 a.m.