[R-meta] moderator and adjusted effects
chr|@t|@n@roever @end|ng |rom med@un|-goett|ngen@de
Mon Dec 14 20:37:56 CET 2020
another difference between your options (1) and (2) is that in case of
(1) you get three different, independent estimates of the heterogeneity
(tau), whereas in (2) you assume a common heterogeneity parameter for
all three groups.
In case you have "many" studies in each group (say, 20), it may not
make much of a difference, but if you have "few" studies (say, 5) in
some, and the assumption of a common heterogeneity parameter seems
plausible, then borrowing information on the heterogeneity across the
three groups may help.
On Mon, 2020-12-14 at 18:18 +0000, Michael Dewey wrote:
> Dear Valeria
> Comments in-line
> On 14/12/2020 17:13, Valeria Ivaniushina wrote:
> > Dear experts,
> > In my sample of articles for meta-analysis there are three
> > categories, or
> > three conditions, that may influence the effect of interest.
> > I am more interested in estimating different effects from these
> > conditions
> > than in explaining heterogeneity in effect sizes.
> > 1) I can do a meta-analysis for each of these conditions separately
> > and get
> > three different mean effect sizes.
> > 2) Or I can do a meta-analysis of the whole sample, then include a
> > condition as a moderator and calculate adjusted effects as
> > described here :
> I would go for option two as it will give you estimates of the
> differences between the levels of your moderator.
> > Which option is better?
> > Additional question: when I include a categorical moderator, is it
> > the
> > same as including a dummy variable in a regression?
> > How can I specify that the variable is categorical with 3 levels?
> If you make the moderator a factor then R will take care of this for
> > Best,
> > Valeria
> > [[alternative HTML version deleted]]
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