[R-meta] Inner|outer model vs multiple random id terms in rma.mv

[Divya Ravichandar]

Thank you Prof.Wolfgang. I was wondering how one would interpret negative
rho (does this imply there is negative correlation between the inner
levels?)
Also for a case where rho is negative is there a preference on whether the
`~inner| outer` or  `~1| inner , ~1|outer` is more applicable?

On Wed, Apr 29, 2020 at 10:45 AM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

> Hi Divya,
>
> These two formulations will only yield the same results when rho is
> estimated to be >= 0 (which is not the case in the second example).
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: R-sig-meta-analysis [mailto:
> r-sig-meta-analysis-bounces using r-project.org]
> >On Behalf Of Divya Ravichandar
> >Sent: Wednesday, 29 April, 2020 19:00
> >To: r-sig-meta-analysis using r-project.org
> >Subject: Re: [R-meta] Inner|outer model vs multiple random id terms in
> >rma.mv
> >
> >Hi all
> >
> >Following a recommendation from Prof.Wolfgang to make access to input data
> >easier, I have reformatted the above example to avoid using an external
> csv
> >file and such.
> >
> >Hi all,
> >
> >I am trying to understand why results from running a model of the form
> >~lvl1|lv2 are not comparable to results of running ~1 | lvl1 ,~ 1 | lvl2
> >
> >In a simple example (case_simple in code below),results of the 2 models
> are
> >comparable as expected.
> >However, when running the 2 models on a more complex example
> (case_complex)
> >markedly different results are obtained with ~ Dataset | Cohort estimating
> >a pvalue of .02 and list(~ 1 | Dataset,~ 1 | Cohort) estimating a pvalue
> of
> >.2
> >
> >Thank you
> >
> >*Reproducible example*
> >library(metafor)
> ># example where results of the 2 models agree
> >case_simple <- data.frame(Dataset=
> >c("a","b","c","d"),Cohort=c("c1","c1","c2","c3"), Tech=
> >c("a1","a2","a1","a1"),Effect_size=c(-1.5,-
> >3,1.5,3),Standard_error=c(.2,.4,.2,.4))
> >res1 = rma.mv(Effect_size, Standard_error^2, random = list(~ 1 |
> Dataset,~
> >1 | Cohort), data=case_simple)
> >res2=rma.mv(Effect_size, Standard_error^2, random = ~ Dataset | Cohort,
> >data=case_simple)
> >
> ># example where results of the 2 models DONT agree
> >case_complex <-
>
> >data.frame(Dataset=c("Dt1","Dt2","Dt3","Dt4","Dt5","Dt5","Dt6","Dt7","Dt8","
> >Dt9"),Cohort=c("C1","C2",rep("C3",5),rep("C4",2),"C5"),
> >
> >Effect_size=c(-0.002024454,-0.003915314,-0.042282757,-1.43826175,-
> >0.045423574,-0.17682309,-21.72691245,-2.559727204,-0.091972279,-
> >0.763332081),
> >
>
> >Standard_error=c(0.15283972,0.117452325,0.262002289,0.555230971,0.708917912,
> >0.682989908,2.704749864,1.40514335,0.735696048,0.713557015))
> >res1 = rma.mv(Effect_size, Standard_error^2, random = list(~ 1 |
> Dataset,~
> >1 | Cohort), data=case_complex)
> >res2=rma.mv(Effect_size, Standard_error^2, random = ~ Dataset | Cohort,
> >data=case_complex)
> >
> >On Wed, Apr 22, 2020 at 9:51 AM Divya Ravichandar <divya using secondgenome.com
> >
> >wrote:
> >
> >> Hi all,
> >>
> >> I am trying to understand why results from running a model of the form
> >> ~lvl1|lv2 are not comparable to results of running ~1 | lvl1 ,~ 1 | lvl2
> >>
> >> In a simple example case below,results of the 2 models are comparable as
> >> expected.
> >>
> >> ```case <- data.frame(Dataset=
> >> c("a","b","c","d"),Cohort=c("c1","c1","c2","c3"), Tech=
> >> c("a1","a2","a1","a1"),Effect_size=c(-1.5,-
> >3,1.5,3),Standard_error=c(.2,.4,.2,.4))
> >> res1 = rma.mv(Effect_size, Standard_error^2, random = list(~ 1 |
> >> Dataset,~ 1 | Cohort), data=case)
> >> res2=rma.mv(Effect_size, Standard_error^2, random = ~ Dataset | Cohort,
> >> data=case)
> >> ```
> >> However, when running the 2 model on a more complex example [attached]
> >> markedly different results are obtained with ~ Dataset | Cohort
> >> estimating a pvalue of .02 and list(~ 1 | Dataset,~ 1 | Cohort)
> >> estimating a pvalue of .2
> >> --
> >> *Divya Ravichandar*
> >> Scientist
> >> Second Genome
>


-- 
*Divya Ravichandar*
Scientist
Second Genome

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