[R] metafor

[Jin Choi]

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.viechtbauer at 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-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
>> On Behalf Of Jin Choi
>> Sent: Saturday, May 05, 2012 19:04
>> To: r-help at 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
>>
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