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

[Divya Ravichandar]

By more applicable, I was trying to get clarity around how to interpret the
results of the two model when rho is < 0.

The ~inner| outer in this example estimated a negative rho value and
profiling indicated that this was indeed the correct estimate. In that
case, does using `~1| inner , ~1|outer` formulation on this dataset
invalid considering the assumption of rho>0 is not in line with the
estimated value of rho < 0 by the inner|outer model

On Wed, Apr 29, 2020 at 11:32 AM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

> Yes, negative rho means negative correlation (rho is the correlation
> between random effects that have the same level of the 'outer' variable).
>
> I don't know what you mean with "more applicable". The '~1| inner ,
> ~1|outer' formulation implicitly assumes that the correlation is >= 0. If
> this is what you would like to assume, then you can use this formulation.
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: Divya Ravichandar [mailto:divya using secondgenome.com]
> >Sent: Wednesday, 29 April, 2020 20:15
> >To: Viechtbauer, Wolfgang (SP)
> >Cc: r-sig-meta-analysis using r-project.org
> >Subject: Re: [R-meta] Inner|outer model vs multiple random id terms in
> >rma.mv
> >
> >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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