I would like to conduct a meta-analysis in R of studies using a pre-post treatment design. In every individual study 4 different subgroups of patients have undergone a pharmacological treatment and their symptoms are measured on a continuous scale. For every study mean and sd of the effect size (cohens d) for the treatment effect is available for every group (this makes the analysis for complicated then usual meta-analysis in which only a single outcome measure per study is used).
What is an appropriate way to investigate the effect of treatment (difference between pre- and post-measurement) as well as the effect of group as well as the group-treatment interaction? Would the below example be a easy solution?
Here is some R code for dummy data:
set.seed(123) a <- c(rep("study1",4), rep("study2",4), rep("study3",4), rep("study4",4), rep("study5",4), rep("study6",4)) b <- rep(c("group1", "group2", "group3", "group4"),6) data <- as.data.frame(cbind(a,b)) names(data) <- c("study", "group") data$d <- rep(c(1,2,3,4),6) + rnorm(24,0,0.5) data$sd <- rep(1, 24) + rnorm(24,0,0.25) library(metafor) model_1 <- rma(d, sd, data=data) summary(model_1) forest(model_1) model_2 <- rma(d, sd, mods=group, data=data) summary(model_2) forest(model_2)