[R-meta] Multilevel / Specification of levels
Viechtbauer, Wolfgang (SP)
wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Wed Sep 18 16:14:22 CEST 2019
A group can consist of one member, which is the case here. So, 'id' can also be unique for every row in the dataset. In fact, this is what we do in the standard random-effects model. See:
The model you are using then *adds* random effects at the study level.
From: Dudenhöffer, Tina [mailto:t.dudenhoffer using uva.nl]
Sent: Wednesday, 18 September, 2019 15:40
To: Viechtbauer, Wolfgang (SP)
Cc: Michael Dewey; r-sig-meta-analysis using r-project.org
Subject: Re: [R-meta] Multilevel / Specification of levels
Dear Michael & Wolfgang,
thank you very much for the quick response.
In the meantime I, indeed, proceeded with specifying random effects for each effect size (that is, I included the EScumulative in the model)
I am still stripping over the definition "~ 1 | id is a grouping id” because each effect size is unique and hence there is no grouping ID for those. However, based on your comments, I am glad to see I am specifying the model correctly, by including each effect size.
Many thanks and kind regards,
> On Sep 18, 2019, at 11:23 AM, Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> Michael is absolutely right. You should include the row/estimate-level random effect in the model. See also:
> -----Original Message-----
> From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of Michael Dewey
> Sent: Wednesday, 18 September, 2019 9:47
> To: Dudenhöffer, Tina; r-sig-meta-analysis using r-project.org
> Subject: Re: [R-meta] Multilevel / Specification of levels
> Dear Tina
> My understanding of this is that you do need to specify a random term
> for each effect as well to get the equivalent of a random effects
> meta-analysis. Do doubt someone more expert than I will chip in here if
> that is not correct.
> On 17/09/2019 10:55, Dudenhöffer, Tina wrote:
>> Dear all,
>> I am running a multilevel model using the rma.mv function.
>> res4 <- rma.mv(Hedges_g, Hedges_v, method=“REML", random = list( ~ 1 | Study), data=Data)
>> The question I have is regarding the random argument. If I understand correctly, this is where the levels are specified. My effect sizes (Hedges_g) are nested in studies (Study). The variable Study refers to the unique study ID in my data set. When reading the documentation, it says that the ~ 1 | id is a grouping id and outcomes with the same id receive the same random effect. Hence, in my example, all outcomes from the same study would receive the same random effect.
>> However, I have also seen other models that seem to include the unique effect size ID and if I translate this to my example it would look like this:
>> res4 <- rma.mv(Hedges_g, Hedges_v, method=“REML", random = list(~ 1 | EScumulative, ~ 1 | Study), data=Data)
>> As you can see the difference is the ~ 1 | EScumulative. EScumulative in my data set refers to each unique effect size. Based on the documentation and the notion that the id in the random argument refers to grouping variables, I don’t believe that I should include the EScumulative in the model, because none of them share the same value (unlike the id for Study, where some effect sizes share the same study ID. However, there are some examples (and opinions) that suggest otherwise.
>> That said: My question is, which of the two models is correct?
>> I am now working using the second model (including the EScumulative):
>> The variance components look like this: so to me this almost seems like there is no variation on the study level and I wonder if I should even run multilevel?
>> estim sqrt nlvls fixed factor
>> sigma^2.1 0.2283 0.4778 62 no EffectSizeID
>> sigma^2.2 0.0000 0.0000 28 no Study
>> I am new to Meta Analysis, new to Multilevel and new to R - excuses in case some of this is not clear.
>> Thanks for your help and kind regards,
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