[R-meta] Meta-analysis of diagnostic accuracy studies: Generate bivariate forest plots using R
Dear Megan When you have fitted a bivariate model you need some form of bivariate plot which is not what a forest plot gives you. You can plot the sensitivities and specificities in a univariate way as you have done. For bivariate plots you might explore ?crosshair ?ROCellipse Michael
On 30/09/2018 02:25, Rushkin, Megan C wrote:
Hello,
I would like to obtain forest plots of meta-analyzed test accuracy studies using a bivariate approach in R.
After some research on available R packages for this task, I decided to try out the Reitsma function in the Mada package. While I'm able to fit the model to my data and obtain the summary results, the forest() function does not work for the bivariate model the way it did for the univariate model:
AuditC6 <- data.frame(TP = c(47, 126, 19, 36, 130, 84), FN = c(9, 51, 10, 3,
19, 2), FP = c(101, 272, 12, 78, 211, 68), TN = c(738, 1543, 192, 276, 959, 89))
AuditC6$names <- c("Study 1", "Study 2", "Study 4", "Study 4", "Study 5",
"Study 6")
data("AuditC")
#Univariate (madad function) approach that works:
forest(madad(AuditC), type = "sens")
forest(madad(AuditC), type = "spec")
#Bivariate (reitsma function) approach that does not work:
forest(reitsma(AuditC), type = "sens")
forest(reitsma(AuditC), type = "spec")
Resulting error: Error in UseMethod("forest") : no applicable method for 'forest' applied to an object of class "reitsma"
Could someone help me to identify what I'm doing wrong here, or if there is another method to obtain these plots?
Thank you for your time.
Megan
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