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

[Jack Solomon]

Thank you, Michael, for sharing this useful information.

Jack

On Fri, Jun 25, 2021 at 7:26 AM Michael Dewey <lists using dewey.myzen.co.uk>
wrote:

> Just to add an extra possibility ehre.
>
> Suppose you have studies using different controls like, say, treatment
> as usual, waiting list, active placebo. Some studies use one but some
> studies use two or three of them. You could view this in a network
> meta-analysis framework. Barth and colleagues (PLOS Medicine, 2013 vol
> 10  (5) have an interesting example where they did not have any
> head-to-head comparisons between the three controls but NMA did show
> them to be different.
>
> Michael
>
> On 24/06/2021 22:52, Jack Solomon wrote:
> > Thank you very much for the clarification. That makes perfect sense.
> >
> > Jack
> >
> > On Thu, Jun 24, 2021 at 4:44 PM James Pustejovsky <jepusto using gmail.com>
> wrote:
> >
> >> The random effect for controlID is capturing any heterogeneity in the
> >> effect sizes across control groups nested within studies, *above and
> beyond
> >> heterogeneity explained by covariates.* Thus, if you include a
> covariate to
> >> distinguish among types of control groups, and the differences between
> >> types of control groups are consistent across studies, then the
> covariate
> >> might explain all (or nearly all) of the variation at that level, which
> >> would obviate the purpose of including the random effect at that level.
> >>
> >> On Thu, Jun 24, 2021 at 9:56 AM Jack Solomon <kj.jsolomon using gmail.com>
> >> wrote:
> >>
> >>> 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?
> >>>
> >>> Thanks, Jack
> >>>
> >>> On Thu, Jun 24, 2021 at 9:17 AM James Pustejovsky <jepusto using gmail.com>
> >>> wrote:
> >>>
> >>>> Hi Jack,
> >>>>
> >>>> Responses inline below.
> >>>>
> >>>> James
> >>>>
> >>>>
> >>>>> 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?
> >>>>
> >>>
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-meta-analysis mailing list
> > R-sig-meta-analysis using r-project.org
> > https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis
> >
>
> --
> Michael
> http://www.dewey.myzen.co.uk/home.html
>

	[[alternative HTML version deleted]]