[R-meta] Studies with more than one control group
kj@j@o|omon @end|ng |rom gm@||@com
Thu Jun 24 16:56:08 CEST 2021
Thank you James. On my question 3, I was implicitly referring to my
previous question (a previous post titled: Studies with independent
samples) regarding the fact that if I decide to drop 'sampleID', then I
need to change the coding of the 'studyID' column (i.e., then, each sample
should be coded as an independent study). So, in my question 3, I really
was asking that in the case of 'controlID', removing it doesn't require
changing the coding of any other columns in my data.
Regarding adding 'controlID' as a random effect, you said: "... an
additional random effect for controlID will depend on how many studies
include multiple control groups and whether the model includes a covariate
to distinguish among types of control groups (e.g., business-as-usual
versus waitlist versus active control group)."
I understand that the number of studies with multiple control groups is
important in whether to add a random effect or not. But why having "a
covariate to distinguish among types of control groups" is important in
whether to add a random effect or not?
On Thu, Jun 24, 2021 at 9:17 AM James Pustejovsky <jepusto using gmail.com> wrote:
> Hi Jack,
> Responses inline below.
>> I have come across a couple of primary studies in my meta-analytic pool
>> that have used two comparison/control groups (as the definition of
>> 'control' has been debated in the literature I'm meta-analyzing).
>> (1) Given that, should I create an additional column ('control') to
>> distinguish between effect sizes (SMDs in this case) that have been
>> obtained by comparing the treated groups to control 1 vs. control 2 (see
> Yes. Along the same lines as my response to your earlier question, it
> seems prudent to include ID variables like this in order to describe the
> structure of the included studies.
>> (2) If yes, then, does the addition of a 'control' column call for the
>> addition of a random effect for 'control' of the form: "~ |
>> studyID/controlID" (to be empirically tested)?
> I expect you will find differences of opinion here. Pragmatically, the
> feasibility of estimating a model with an additional random effect for
> controlID will depend on how many studies include multiple control groups
> and whether the model includes a covariate to distinguish among types of
> control groups (e.g., business-as-usual versus waitlist versus active
> control group).
> At a conceptual level, omitting random effects for controlID leads to
> essentially the same results as averaging the ES across both control
> groups. If averaging like this makes conceptual sense, then omitting the
> random effects might be reasonable.
>> (3) If I later decide to drop controlID from my dataset, I think I can
>> still keep all effect sizes from both control groups intact without any
>> changes to my coding scheme, right?
> I don't understand what you're concern is here. Why not just keep
> controlID in your dataset as a descriptor, even if it doesn't get used in
> the model?
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