# lgRR.vcov: Computing Variance-Covariance Matrices for Log Risk Ratios In metavcov: Computing Variances and Covariances, Visualization and Missing Data Solution for Multivariate Meta-Analysis

 lgRR.vcov R Documentation

## Computing Variance-Covariance Matrices for Log Risk Ratios

### 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 log risk ratio (or log relative risk). See mix.vcov for effect sizes of the same or different types.

### Usage

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


### 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. st  A N \times 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 \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.

### Value

  ef A N \times p data frame whose columns are computed log risk ratios. 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 log risk ratios 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 <- lgRR.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 log risk ratio as y
y <- computvcov$ef colnames(y) = c("lgRR.DD", "lgRR.D") # name variance-covariance matrix of trnasformed z scores as covars S <- computvcov$matrix.vcov
## fixed-effect model
MMA_FE <- summary(metafixed(y = y, Slist = computvcov$list.vcov)) MMA_FE ####################################################################### # Running random-effects model using package "mixmeta" or "metaSEM" ####################################################################### #library(mixmeta) #mvmeta_RE = summary(mixmeta(cbind(lgRR.DD, lgRR.D)~1, # S = S, data = as.data.frame(y), # method = "reml")) #mvmeta_RE # maximum likelihood estimators from the metaSEM package # library(metaSEM) # metaSEM_RE <- summary(meta(y = y, v = S)) # metaSEM_RE ############################################################## # Plotting the result: ############################################################## obj <- MMA_FE # obj <- mvmeta_RE # obj <- metaSEM_RE # pdf("CI.pdf", width = 4, height = 7) plotCI(y = computvcov$ef, v = computvcov$list.vcov, name.y = c("lgRR.DD", "lgRR.D"), name.study = Geeganage2010$studyID,
y.all = obj$coefficients[,1], y.all.se = obj$coefficients[,2],
hline = 1)
# dev.off()


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