[R-sig-ME] Multiple comparison correction?
ahnate at gmail.com
Sat Nov 8 00:44:37 CET 2014
Thank you for your replies. Gelman's 2012 paper is the one I had come
across earlier. I guess I'm wondering whether our situation would be one
where it would be acceptable not to use multiple comparison correction? Or
perhaps some rule of thumb to use to evaluate when it would be acceptable?
I haven't worked through the entirety of Gelman's papers yet. I have used
lsmeans on lme objects before, but am not as familiar with glht.
Our dataset involves repeated measures (at variable time points) of
cortical brain measurements for children over several years. We are
interested in effects of gender and prenatal drug exposure on these regions
of interest measurements (~200 indices of brain thickness, area, and
volume). Of the 200 measurements, there are about 8 with significant group
results, p-values between .05 and .001, hence the concern about multiple
On Fri, Nov 7, 2014 at 4:38 AM, Daniel Wright <Daniel.Wright at act.org> wrote:
> Here (
> is a more recent discussion of Gelman's view on topic.
> Daniel B. Wright, Ph.D.
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> -----Original Message-----
> From: r-sig-mixed-models-bounces at r-project.org [mailto:
> r-sig-mixed-models-bounces at r-project.org] On Behalf Of Malcolm Fairbrother
> Sent: Thursday, November 06, 2014 4:39 PM
> To: ahnate at gmail.com
> Cc: r-sig-mixed-models
> Subject: Re: [R-sig-ME] Multiple comparison correction?
> Dear Ahnate,
> I'm not a particular expert on this topic, but I found Gelman et al.'s
> views quite interesting:
> >From the sounds of it you'll find the paper useful too.
> > Date: Wed, 5 Nov 2014 12:49:33 -1000
> > From: Ahnate Lim <ahnate at gmail.com>
> > To: r-sig-mixed-models at r-project.org
> > Subject: [R-sig-ME] Multiple comparison correction?
> > Hello,
> > I have a question related to mixed effect modeling and how to do
> > multiple comparisons.
> > We have a longitudinal study with different groups and many dependent
> > variables such as of brain cortical volume in different areas, etc.
> > I am using lme, and remember reading somewhere that multiple
> > comparison corrections do not actually apply to linear mixed effects
> > models, due to the statistics involved.
> > For example, if I run the same model on 100 dependent variables,
> > traditionally I would need to correct for multiple comparisons by
> > dividing the alpha level (0.05) by 100 to get the proper criterion of
> > 0.0005, adjusting for the increased likelihood of getting type I
> > errors. I am wondering however, if this process is the same, or even
> > necessary at all for lme models?
> > Thank you,
> > Ahnate
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