Dear colleges,
I'm trying to build a pipeline (or at least a more automatize approach)
that facilitate the data transformation? (from raw data to TE and seTE)
in R for a Network MA.
My starting point is the example that Guido present in Chapter 8 (here
<https://www.uniklinik-freiburg.de/fileadmin/mediapool/08_institute/biometrie-statistik/Dateien/Englisch/Studies_and_Teaching/Educational_Books/Meta-Analysis_with_R/R_Code/08-network.R>):
# help(Senn2013)
willms <- data.frame(treatment=c("metf", "acar", "plac"),
???????????????????? n=c(29, 31, 29),
???????????????????? mean=c(-2.5, -2.3, -1.3),
???????????????????? sd=c(0.862, 1.782, 1.831),
???????????????????? stringsAsFactors=FALSE)
willms
(comp12 <- metacont(n[1], mean[1], sd[1], n[2], mean[2],
sd[2],data=willms, sm="MD"))
(comp13 <- metacont(n[1], mean[1], sd[1], n[3], mean[3],
sd[3],data=willms, sm="MD"))
(comp23 <- metacont(n[2], mean[2], sd[2], n[3], mean[3],
sd[3],data=willms, sm="MD"))
TE <- c(comp12$TE, comp13$TE, comp23$TE)
seTE <- c(comp12$seTE, comp13$seTE, comp23$seTE)
Is there a more efficient way to produce the TE and seTE with escalc?
For example, this does not gives the same seTE values:
escalc(measure="MD",n1i=n[1], m1i=mean[1],
?????? sd1i=sd[1], n2i=n[2], m2i=mean[2], sd2i=sd[2],
?????? data=willms)
escalc(measure="MD",n1i=n[1], m1i=mean[1],
?????? sd1i=sd[1], n2i=n[3], m2i=mean[3], sd2i=sd[3],
?????? data=willms)
escalc(measure="MD",n1i=n[2], m1i=mean[2],
?????? sd1i=sd[2], n2i=n[3], m2i=mean[3], sd2i=sd[3],
?????? data=willms)
Maybe with Stata?
Thanks in advance for your valuable help!