# md.vcov: Computing Variance-Covariance Matrices for Mean Differences In metavcov: Computing Variances and Covariances, Visualization and Missing Data Solution for Multivariate Meta-Analysis

 md.vcov R Documentation

## Computing Variance-Covariance Matrices for Mean Differences

### Description

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.

### Usage

md.vcov(r, nt, nc, n_rt = NA, n_rc = NA, sdt, sdc)


### Arguments

 r  A N-dimensional list of p \times 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 \times 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. n_rt  A N-dimensional list of p \times 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. sdt  A N \times p matrix storing sample standard deviations for each outcome from treatment group. sdt[i,j] is the sample standard deviation from study i for outcome j. If outcome j is not continuous such as MD or SMD, NA has to be imputed in the jth column. sdc  A matrix defined in a similar way as sdt for the control group.

### Value

 list.vcov  A N-dimensional list of p(p+1)/2 \times p(p+1)/2 matrices of computed variance-covariance matrices. matrix.vcov  A N \times p(p+1)/2 matrix whose rows are computed variance-covariance vectors.

Min Lu

### References

Lu, M. (2023). Computing within-study covariances, data visualization, and missing data solutions for multivariate meta-analysis with metavcov. Frontiers in Psychology, 14:1185012.

### Examples

######################################################
# 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 "mixmeta" or "metaSEM"
#######################################################################
# Restricted maximum likelihood (REML) estimator from the mixmeta package
#library(mixmeta)
#mvmeta_RE <- summary(mixmeta(cbind(MD_SBP, MD_DBP)~1, 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])


metavcov documentation built on July 9, 2023, 7:11 p.m.