[R-meta] Mismatch between output from sub-group analysis and forest plot

[Viechtbauer, Wolfgang (SP)]

Dear Joao,

Once again, please post in plain text. See below how your post looks on my end. It is very hard to read.

If you are using Gmail directly, you can click on the three dots in the New Message window (at the bottom right, next to the trashcan symbol) and select "Plain text mode" there.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of Joao Afonso
Sent: Tuesday, 11 February, 2020 18:51
To: r-sig-meta-analysis using r-project.org
Subject: [R-meta] Mismatch between output from sub-group analysis and forest plot

Dear all,

I am conducting a meta-analysis on prevalence and incidence data. As there
is plenty of heterogeneity I am doing a sub-group analysis. However I am
finding the output of the same and forest plots produced to have different
values for the pooled estimates. I am using the double arcsine
transformation and then have the data back-transformed to produce the
estimates. The model is the following:

*iies.da.lcmbi<-ies.da %>%  filter(is.na
<http://is.na>(lcmbi)==FALSE)subganal.lcmbi=rma(yi, vi, data=ies.da.lcmbi,
mods=~lcmbi, method="DL")pes.da.lcm=rma(yi, vi, data=ies.da.lcmbi,
mods=~lcmbi=="Records", method="DL")pes.da.records=rma(yi, vi,
data=ies.da.lcmbi, mods=~lcmbi=="Locomotion Scoring Method",
method="DL")pes.subg.lcmbi=predict(subganal.lcmbi,transf=transf.ipft.hm
<http://transf.ipft.hm>,targ=list(ni=prevalence_2020_cow_nomv$ssizeanimal))dat.samevar=data.frame(estimate=c((pes.da.lcm$b)[1],
(pes.da.records$b)[1]),stderror=c((pes.da.lcm$se)[1],
(pes.da.records$se)[1]),tau2=subganal.lcmbi$tau2)pes.da.lcmbi=rma(estimate,
sei=stderror,method="DL",data=dat.samevar)pes.lcmbi=predict(pes.da.lcmbi,
transf=transf.ipft.hm <http://transf.ipft.hm>,
targ=list(ni=prevalence_2020_cow_nomv$ssizeanimal))print(pes.da.lcm,
digits=3) print(pes.da.records, digits=3) print(subganal.lcmbi,
digits=3)print(pes.lcmbi, digits=3) *

And to produce the forest plot I use the following code:

*subganal.lcmbi=rma(yi, vi, data=ies.da.lcmbi, mods=~lcmbi, method="DL")*
*pes.summary=metaprop(*nlameanimal*, *ssizeanimal*, author,
data=ies.da.lcmbi,*
*                     sm="PFT",*
*                     method.tau="DL",*
*                     method.ci <http://method.ci>="NAsm",*
*                     byvar=lcmbi,*
*                     tau.common=TRUE,*
*                     tau.preset=sqrt(subganal.lcmbi$tau2))*
*precision=sqrt(ies.da.lcmbi$vi)*
*jpeg(file="forest_cow_cow_lcmbi.jpeg", width=11, height=12, units="in",
res=300)*
*forest(pes.summary,*
*       xlim=c(0,1),*
*       pscale=1,*
*       rightcols=FALSE,*
*       leftcols=c("studlab", "effect", "ci"),*
*       leftlabs=c("Study", "Prevalence", "95% C.I."),*
*       text.random="Prevalence of Lameness in British Dairy Cattle",*
*       bylab = "Lameness Data Source",*
*       print.byvar = TRUE,*
*       xlab="Prevalence of Lameness", smlab="",*
*       weight.study="random", squaresize=0.5, col.square="navy",*
*       col.diamond="maroon", col.diamond.lines="maroon",*
*       pooled.totals=FALSE,*
*       comb.fixed=FALSE,*
*       fs.hetstat=10,*
*       print.tau2=TRUE,*
*       print.Q=TRUE,*
*       print.pval.Q=TRUE,*
*       print.I2=TRUE,*
*       digits=2,*
*       sortvar = precision)*
*dev.off()*

What could I be doing wrong? I will be extremely grateful for any help or
guidance.

Wishing all a great afternoon,

Joao
-- 
João Afonso
DVM, MSc Veterinary Epidemiology
PhD Student
Department of Infection and Global Health
University of Liverpool
+351914812305