I tried the subset argument as Michael suggested, which led to the same results. The results between meta-regression and subgroup analyses were only slightly different as Wolfgang had suggested. I also believe that these minor differences must be arising from the use of random effects.
Thank you very much to both of you! Jin On Mon, May 7, 2012 at 5:14 AM, Viechtbauer Wolfgang (STAT) <wolfgang.viechtba...@maastrichtuniversity.nl> wrote: > Michael just provided a good suggestion, using the subset argument to make > sure that you are really using the same data in both analyses. > > However, I would not expect the results to be exactly the same anyway. > Remember that these are random/mixed-effects models you are using. So, when > you use the meta-regression approach, you are estimating tau^2 differently > than when using just the subset of data on European women. Essentially, the > meta-regression approach assumes that the amount of heterogeneity is the same > within each of the 4 possible combinations of sex and race (I am assuming > that race is coded dichotomously). When you just use the subset of data on > European women, you are estimating tau^2 just for that particular subgroup. > Since the estimate of tau^2 is probably not the same with these two > approaches, you will also get (slightly?) different results. > > Here is an example from one of the datasets that comes with the metafor > package to illustrate this: > > ######################################################################## > > library(metafor) > > ### load empint data > data(dat.empint) > > ### calculate r-to-z transformed correlations and corresponding sampling > variances > dat <- escalc(ri=ri, ni=ni, measure="ZCOR", data=dat.empint, append=TRUE) > > ### remove studies where struct is NA > dat <- dat[!is.na(dat$struct ),] > > ### mixed-effects model with struct as moderator > res <- rma(yi, vi, mods=~relevel(factor(struct), ref="u"), data=dat) > res$tau2 > predict(res, transf=transf.ztor, newmods=c(0,1), digits=2) > > ### two separate random-effects models within each level of struct > res.u <- rma(yi, vi, data=dat, subset=struct=="u") > res.s <- rma(yi, vi, data=dat, subset=struct=="s") > res.u$tau2 > predict(res.u, transf=transf.ztor, digits=2) > res.s$tau2 > predict(res.s, transf=transf.ztor, digits=2) > > ######################################################################## > > Note that the results are similar, but not quite the same. This is a result > of the estimate of tau^2 being different in the two subgroups from the tau^2 > estimated in the meta-regression model. To illustrate this further, try this > out (which simply sets the estimate of tau^2 in the two subgroup models equal > to the estimate of tau^2 from the mixed-effects model): > > ######################################################################## > > res.u <- rma(yi, vi, data=dat, subset=struct=="u", tau2=res$tau2) > res.s <- rma(yi, vi, data=dat, subset=struct=="s", tau2=res$tau2) > res.u$tau2 > predict(res.u, transf=transf.ztor, digits=2) > res.s$tau2 > predict(res.s, transf=transf.ztor, digits=2) > > ######################################################################## > > Now you will find that the results are exactly the same with the > meta-regression and the subgrouping approach. This may lead one to think that > subgrouping is the preferred approach (since it does not assume that tau^2 is > the same in the subgroups). However, the precision of the estimate of tau^2 > tends to be determined largely by k (the number of studies/observations). > Since the overall k is often not very large to begin with (the example used > above is actually a rather large meta-analysis), subgrouping makes the k > within each subgroup even smaller, leading to even less precise estimates of > tau^2. Hopefully, the actual *conclusions* are not affected by the approach > used. > > Best, > > Wolfgang > > -- > Wolfgang Viechtbauer, Ph.D., Statistician > Department of Psychiatry and Psychology > School for Mental Health and Neuroscience > Faculty of Health, Medicine, and Life Sciences > Maastricht University, P.O. Box 616 (VIJV1) > 6200 MD Maastricht, The Netherlands > +31 (43) 388-4170 | http://www.wvbauer.com > > >> -----Original Message----- >> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] >> On Behalf Of Jin Choi >> Sent: Saturday, May 05, 2012 19:04 >> To: r-help@r-project.org >> Subject: [R] metafor >> >> Dear users of metafor, >> >> I am working on a meta-analysis using the metafor package. I have a excel >> csv database that I am working with. I am interested in pooling the effect >> measures for a particular subgroup (European women) in this csv database. >> I am conducting both sub-group and meta-regression. >> >> In subgroup-analyses, I have stratified the database to create a separate >> csv file just for European women from the original database and conducted >> the following: >> >> women_west<-read.csv("women_west.csv") >> print(women_west) >> dat<-escalc(measure="ZCOR",ri=Pearson,ni=N,data=women_west,append=TRUE) >> res<-rma(yi,vi,data=dat) >> is.factor(dat$year) >> forest(res,transf=transf.ztor) >> >> In meta-regression, I used the original database, but used categorical >> moderators for sex (=women), and ethnicity (=european) to find the effect >> specifically in European women. >> adult<-read.csv("adult.csv") >> print(adult) >> dat<-escalc(measure="ZCOR",ri=Pearson,ni=N,data=adult,append=TRUE) >> res<-rma(yi,vi,data=dat) >> res<-rma(yi,vi,mods=cbind(sex,race),data=dat) >> predict(res,transf=transf.ztor,newmods=cbind(seq(from=0,to=1,by=1),1),addx >> =TRUE) >> >> I am getting different results between the forest function from subgroup >> analyses, and the predict function from the meta-regression. >> I thought they should have been the same - can I get help to explain why >> there are differences? In both cases, I am transforming raw Pearson >> coefficients to z-transformed coefficients, then back-transforming to raw >> r after pooling. >> >> Thank you very much. >> >> Jin Choi >> MSc (Epidemiology) Student >> McGill University, Montreal CANADA >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting- >> guide.html >> and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
