[R-meta] Studies with more than one control group

James Pustejovsky jepu@to @end|ng |rom gm@||@com
Thu Jun 24 16:17:47 CEST 2021

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
> below)?
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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