[R-meta] Dealing with effect size dependance with a small number of studies
Danka Puric
dj@gu@rd @end|ng |rom gm@||@com
Tue Feb 9 12:57:27 CET 2021
Dear Wolfgang,
thanks a lot!
We used a slightly different scheme for coding:
IDstudy IDeffect
1 11
1 12
2 21
3 31
3 32
3 33
4 41
4 42
but it's still explicit coding, so it's good to know that the two models
are identical. Nevertheless, we will report the full model.
All the best,
Danka
On Tue, Feb 9, 2021 at 12:41 PM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> Dear Danka,
>
> Indeed, when a variance component in such a model is estimated to be zero,
> then this is the same as dropping this particular random effect from the
> model. Whether your two models below are really identical though depends on
> how you coded the ID variables. There is what could be called implicit and
> explicit coding of the levels. Implicit coding would for example be:
>
> IDstudy IDeffect
> 1 1
> 1 3
> 2 1
> 3 1
> 3 3
> 3 4
> 4 1
> 4 2
>
> and then using 'random = ~ 1 | IDstudy / IDeffect'.
>
> Explicit coding would be:
>
> IDstudy IDeffect
> 1 1
> 1 2
> 2 3
> 3 4
> 3 5
> 3 6
> 4 7
> 4 8
>
> Then one can still use 'random = ~ 1 | IDstudy / IDeffect' or equivalently
> 'random = list(~ 1 | IDstudy, ~ 1 | IDeffect)'.
>
> If the IDstudy variance component is estimated to be 0, then this is
> identical to 'random = ~ 1 | IDeffect' **only under explicit coding**. If
> implicit coding was used, then one would have to use, for example, 'random
> = ~ 1 | interaction(IDstudy, IDeffect)'.
>
> So, in your case, if you used implicit coding (so that IDeffect jumps back
> to 1 when IDsubsample changes), then the two would not be the same.
>
> As for what to report: I would also report the results from the full model.
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: R-sig-meta-analysis [mailto:
> r-sig-meta-analysis-bounces using r-project.org] On
> >Behalf Of Danka Puric
> >Sent: Tuesday, 09 February, 2021 11:15
> >To: R meta
> >Subject: Re: [R-meta] Dealing with effect size dependance with a small
> number of
> >studies
> >
> >Hi everyone,
> >
> >I have a (hopefully short) additional question. I just recently
> >remembered that we have another level of potential effect size
> >dependence in our data - the level of the journal article / paper.
> >Therefore, the theoretically most complete model would be:
> >es <- rma.mv(ES_g, V, random = ~ 1 | IDpaper / IDstudy / IDsubsample /
> >IDeffect, data=MA_dat)
> >
> >For this model I'm getting zero variance (to four decimal places) for
> >IDstudy and IDsubsample random effects, which makes it (from what I
> >can tell) numerically identical to this simplified model:
> >es <- rma.mv(ES_g, V, random = ~ 1 | IDpaper / IDeffect, data=MA_dat)
> >
> >I was planning on reporting the full model in the manuscript, noting
> >that the variances at certain levels are zero. When testing for the
> >effects of moderators I would also include all levels. Is this the
> >right way to go about this?
> >
> >Thanks in advance,
> >Danka
>
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