[R-meta] effect size transformation for network MA

[Philippe Tadger]

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!

-- 
Kind regards/Saludos cordiales
*Philippe Tadger*
ORCID <https://orcid.org/0000-0002-1453-4105>, Reseach Gate 
<https://www.researchgate.net/profile/Philippe-Tadger>
Phone/WhatsApp: +32498774742

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