[R-meta] Question on combining means with robumeta

[...]
What puzzels me is the difference in the hetereogeneity estimates 
(Tau?, I?..etc.). If I perform the analysis with the non-transformed 
means I get different estimates then the analysis with 
log-transformation.

If I use the metamean function in meta (for comparison) it shows the 
same heterogeneity estimates wheter I log-transform or not.

Regards,
Tobias
*Gesendet:*?Mittwoch, 18. August 2021 um 13:59 Uhr
*Von:*?"Viechtbauer, Wolfgang (SP)" 
<wolfgang.viechtbauer at maastrichtuniversity.nl>
*An:*?"Tobias Saueressig" <t.saueressig at gmx.de>, "Meta list" 
<r-sig-meta-analysis at r-project.org>
*Betreff:*?RE: [R-meta] Question on combining means with robumeta
Dear Tobias,

I don't understand what you mean by "Since in some cases I had 
negative values". Negative values where?

This aside, 'var.eff.size' is for the *variances* but SEM is the SE of 
the means. So it should be:

res <- robu(formula = Mean ~ 1, data = dat,
studynum = Author, var.eff.size =SEM^2,
rho = .8, small = TRUE, modelweights="CORR")
res

although the results are basically the same.

Second, LogMean_SEM doesn't seem to be computed correctly in your 
example and let's not call it SEM since it's a variance:

dat$LogMean_Var <- with(dat, SD^2/(N*Mean^2))

Now:

res1 <- robu(formula = LogMean ~ 1, data = dat,
studynum = Author, var.eff.size = LogMean_Var,
rho = .8, small = TRUE, modelweights="CORR")
res1

#backtransformed results from log scale
round(exp(c(res1$reg_table$b.r[[1]],res1$reg_table$CI.L[[1]],res1$reg_table$CI.U[[1]])),2)

gives very similar results as 'res' earlier.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis 

[mailto:r-sig-meta-analysis-bounces at r-project.org] On

Behalf Of Tobias Saueressig
Sent: Tuesday, 17 August, 2021 17:48
To: Meta list
Subject: [R-meta] Question on combining means with robumeta

Dear All,

I have a question concerning the combination/pooling of means. Since 

most of my

studies are correlated I deceided to use robust variance estimation 

method to do

this. So metamean function in meta was out because of this. So I used 

robumeta.

Since in some cases I had negative values I deceided to use log 

transformed means

for pooling. My problem is that the estimates look reasonable now 

with robumeta

but the heterogeneity parameters(e.g. Tau, I?) are all estimated with 

zero. This

is strange. Does anybody know why that is?

Regards,
Tobias Saueressig

Reproducible example:

#Example
?Author <-c("Carboch et al. (2019)","Stare et al. (2015)","Mackie 

(2013)","Hornery

et al. (2007)",
"Takahashi et al. ( 2006)","Morante et al. (2005)","O'Donoghue, P. 

and Ingram, B.

(2001)",
"O'Donoghue, P. and Ingram, B. (2001)")

?Mean<-c(5.93,4.40,6.42,6.70,5.10,6.40,5.70,4.90)
?SD<-c(0.67,1.73,1.27,2.20,5.19,1.40,1.00,1.40)
?N<-c(7,5,9,17,16,12,24,35)
?SEM<-c(0.25,0.77,0.42,0.53,1.30,0.40,0.20,0.24)
?LogMean<-c(1.78,1.48,1.86,1.90,1.63,1.86,1.74,1.59)
?LogMean_SEM<-c(1.50,3.40,2.72,3.57,6.62,2.59,1.16,1.16)

?dat<-data.frame(Author,Mean,SD,N,SEM,LogMean,LogMean_SEM)

?#Formulas used
?#SEM<-SD/sqrt(N)
?#LogMean<-log(Mean)
?#LogMean_SEM<-SD^2/(N*Mean^2) => taken from metamean function in meta

?#normalscale
?res <- robu(formula = Mean ~ 1, data = dat,
? ? ? ? ? ? ? studynum = Author, var.eff.size =SEM,
? ? ? ? ? ? ? rho = .8, small = TRUE, modelweights="CORR")
?res

?#logscale
?res1 <- robu(formula = LogMean ~ 1, data = dat,
? ? ? ? ? ? ?studynum = Author, var.eff.size = ?LogMean_SEM,
? ? ? ? ? ? ?rho = .8, small = TRUE, modelweights="CORR")
?res1

?#backtransformed results from log scale
?round(exp(c(res1$reg_table$b.r[[1]],res1$reg_table$CI.L[[1]],res1$reg_table$CI.U[
[1]])),2)