1
$\begingroup$

When I performed a subgroup analysis on a catergorical moderator named "moda" (with two levels:m and n) in my data,

dat=read.csv("D:\\...\\bothlevels.csv",header=T,sep=",")#this is a data composed of single proportions
transf.ies=escalc(measure="PFT",xi=cases,ni=total,data=dat,add=0)#note that I used the double arcsine transformation
transf.pes.m=rma(yi,vi,data=transf.ies,subset=(moda=="m"),method="DL")
transf.pes.n=rma(yi,vi,data=transf.ies,subset=(moda=="n"),method="DL")
pes.m=predict(transf.pes.m,transf=transf.ipft.hm,targ=list(ni=dat$total),digits=4);pes.m
pes.n=predict(transf.pes.n,transf=transf.ipft.hm,targ=list(ni=dat$total),digits=4);pes.n

the results showed that:

pes.m: 
pred ci.lb  ci.ub  cr.lb  cr.ub
0.7641 0.6760 0.8422 0.2769 1.0000
pes.n:
pred  ci.lb  ci.ub  cr.lb  cr.ub
0.5442 0.4727 0.6149 0.1752 0.8872

But, when I separated my data into two csv files according to the levels of the moderator and performed meta-analyses respectively, the estimated average effect sizes and the corresponding CIs became slightly different than before.

dat=read.csv("D:\\...\\levelm.csv",header=T,sep=",")
transf.ies=escalc(measure="PFT",xi=cases,ni=total,data=dat,add=0)
transf.pes=rma(yi,vi,data=transf.ies,method="DL")
pes.m=predict(transf.pes,transf=transf.ipft.hm,targ=list(ni=dat$total));pes.m

pes.m:
pred  ci.lb  ci.ub  cr.lb  cr.ub
0.7647 0.6764 0.8430 0.2764 1.0000

dat=read.csv("D:\\...\\leveln.csv",header=T,sep=",")
transf.ies=escalc(measure="PFT",xi=cases,ni=total,data=dat,add=0)
transf.pes=rma(yi,vi,data=transf.ies,method="DL")
pes.n=predict(transf.pes,transf=transf.ipft.hm,targ=list(ni=dat$total));pes.n

pes.n:
pred  ci.lb  ci.ub  cr.lb  cr.ub
0.5441 0.4727 0.6146 0.1759 0.8864

I wondered how this happened. The issue occurred with or without transformation of the original data. Note that this data contains no proportions of 0 or 1, so I don't think the small discrepancy was due to the adjustment of such proportions.

Below are the csv files of my data:

bothlevels.csv levelm.csv leveln.csv

$\endgroup$
3
  • $\begingroup$ This is explained here: metafor-project.org/doku.php/… $\endgroup$
    – Wolfgang
    Jun 10, 2017 at 19:24
  • $\begingroup$ Did you transform the data ? WHAT do you mean by levels of moderator ? $\endgroup$
    – user10619
    Jul 7, 2017 at 8:53
  • $\begingroup$ Let's say I have a moderator: year of publication. The first level is before 2010 and the second level is after 2010. $\endgroup$
    – Naike Wang
    Jul 8, 2017 at 21:21

1 Answer 1

1
$\begingroup$

The issue here is that if you perform one analysis you get a single estimate of $\tau^2$. If you split the dataset into subsets you get separate estimates for $\tau^2$ in each subset. This leads to different estimates throughout. Note that is all supposing you are fitting random effects models which is in fact the case here.

There is an extensive explanation in the page linked to by Wolfgang which also shows how to get estimates from the combined dataset which match those from the separate subsets by allowing for $\tau^2$ to vary between subsets.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.