<html><head></head><body><div style="font-family: Verdana;font-size: 12.0px;"><div>Dear Wolfgang, </div>
<div> </div>
<div>thank you for your answer. My responses:</div>
<div> </div>
<div>"I don't understand what you mean by "Since in some cases I had negative values". Negative values where?"</div>
<div>----Sorry, that is my fault. The example I have shared does not have this problem (a bit stupid example sorry) but I have a couple of analyses that would lead to negative CIs (e.g. -1 second). So I use the log-transformation.</div>
<div> </div>
<div>RE: Variances/SEM</div>
<div> </div>
<div>---You are of course correct and I have to change that. </div>
<div> </div>
<div> </div>
<div>What puzzels me is the difference in the hetereogeneity estimates (Tau², I²..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.</div>
<div> </div>
<div>Regards,</div>
<div> </div>
<div>Tobias </div>
<div> </div>
<div> </div>
<div>
<div>
<div name="quote" style="margin:10px 5px 5px 10px; padding: 10px 0 10px 10px; border-left:2px solid #C3D9E5; word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;">
<div style="margin:0 0 10px 0;"><b>Gesendet:</b> Mittwoch, 18. August 2021 um 13:59 Uhr<br/>
<b>Von:</b> "Viechtbauer, Wolfgang (SP)" <wolfgang.viechtbauer@maastrichtuniversity.nl><br/>
<b>An:</b> "Tobias Saueressig" <t.saueressig@gmx.de>, "Meta list" <r-sig-meta-analysis@r-project.org><br/>
<b>Betreff:</b> RE: [R-meta] Question on combining means with robumeta</div>
<div name="quoted-content">Dear Tobias,<br/>
<br/>
I don't understand what you mean by "Since in some cases I had negative values". Negative values where?<br/>
<br/>
This aside, 'var.eff.size' is for the *variances* but SEM is the SE of the means. So it should be:<br/>
<br/>
res <- robu(formula = Mean ~ 1, data = dat,<br/>
studynum = Author, var.eff.size =SEM^2,<br/>
rho = .8, small = TRUE, modelweights="CORR")<br/>
res<br/>
<br/>
although the results are basically the same.<br/>
<br/>
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:<br/>
<br/>
dat$LogMean_Var <- with(dat, SD^2/(N*Mean^2))<br/>
<br/>
Now:<br/>
<br/>
res1 <- robu(formula = LogMean ~ 1, data = dat,<br/>
studynum = Author, var.eff.size = LogMean_Var,<br/>
rho = .8, small = TRUE, modelweights="CORR")<br/>
res1<br/>
<br/>
#backtransformed results from log scale<br/>
round(exp(c(res1$reg_table$b.r[[1]],res1$reg_table$CI.L[[1]],res1$reg_table$CI.U[[1]])),2)<br/>
<br/>
gives very similar results as 'res' earlier.<br/>
<br/>
Best,<br/>
Wolfgang<br/>
<br/>
>-----Original Message-----<br/>
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces@r-project.org] On<br/>
>Behalf Of Tobias Saueressig<br/>
>Sent: Tuesday, 17 August, 2021 17:48<br/>
>To: Meta list<br/>
>Subject: [R-meta] Question on combining means with robumeta<br/>
><br/>
>Dear All,<br/>
><br/>
>I have a question concerning the combination/pooling of means. Since most of my<br/>
>studies are correlated I deceided to use robust variance estimation method to do<br/>
>this. So metamean function in meta was out because of this. So I used robumeta.<br/>
>Since in some cases I had negative values I deceided to use log transformed means<br/>
>for pooling. My problem is that the estimates look reasonable now with robumeta<br/>
>but the heterogeneity parameters(e.g. Tau, I²) are all estimated with zero. This<br/>
>is strange. Does anybody know why that is?<br/>
><br/>
>Regards,<br/>
>Tobias Saueressig<br/>
><br/>
>Reproducible example:<br/>
><br/>
>#Example<br/>
> Author <-c("Carboch et al. (2019)","Stare et al. (2015)","Mackie (2013)","Hornery<br/>
>et al. (2007)",<br/>
>"Takahashi et al. ( 2006)","Morante et al. (2005)","O'Donoghue, P. and Ingram, B.<br/>
>(2001)",<br/>
>"O'Donoghue, P. and Ingram, B. (2001)")<br/>
><br/>
> Mean<-c(5.93,4.40,6.42,6.70,5.10,6.40,5.70,4.90)<br/>
> SD<-c(0.67,1.73,1.27,2.20,5.19,1.40,1.00,1.40)<br/>
> N<-c(7,5,9,17,16,12,24,35)<br/>
> SEM<-c(0.25,0.77,0.42,0.53,1.30,0.40,0.20,0.24)<br/>
> LogMean<-c(1.78,1.48,1.86,1.90,1.63,1.86,1.74,1.59)<br/>
> LogMean_SEM<-c(1.50,3.40,2.72,3.57,6.62,2.59,1.16,1.16)<br/>
><br/>
> dat<-data.frame(Author,Mean,SD,N,SEM,LogMean,LogMean_SEM)<br/>
><br/>
> #Formulas used<br/>
> #SEM<-SD/sqrt(N)<br/>
> #LogMean<-log(Mean)<br/>
> #LogMean_SEM<-SD^2/(N*Mean^2) => taken from metamean function in meta<br/>
><br/>
> #normalscale<br/>
> res <- robu(formula = Mean ~ 1, data = dat,<br/>
> studynum = Author, var.eff.size =SEM,<br/>
> rho = .8, small = TRUE, modelweights="CORR")<br/>
> res<br/>
><br/>
> #logscale<br/>
> res1 <- robu(formula = LogMean ~ 1, data = dat,<br/>
> studynum = Author, var.eff.size = LogMean_SEM,<br/>
> rho = .8, small = TRUE, modelweights="CORR")<br/>
> res1<br/>
><br/>
> #backtransformed results from log scale<br/>
> round(exp(c(res1$reg_table$b.r[[1]],res1$reg_table$CI.L[[1]],res1$reg_table$CI.U[<br/>
>[1]])),2)</div>
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