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

## 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

 `1` ```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.

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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48``` ```########################################################################### # 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.