[R-sig-ME] logistic regression on posttest (0, 1) with pretest(0, 1)*Group(Treatment, Ctrl) interaction
@ouhey|@@ghebghoub @end|ng |rom gm@||@com
Sun Apr 21 20:07:32 CEST 2019
Thank you for your email.
I used to think that *posttest ~ pretest*Group* will automatically give
you the main effects of group and pretest without having to set them again
separately in the syntax? Please correct me if I am wrong.
Thank you again,
On Sun, 21 Apr 2019 at 19:02, Sorkin, John <jsorkin using som.umaryland.edu>
> It is both difficult and dangerous to add a comment to a thread that one
> has not followed, and in doing so possibly making an inappropriate
> suggestion. Please forgive what may be an not fully informed thought.
> The model you suggest, posttest ~ pretest*Group (ignoring random
> effects) is unusual. In a model that contains an interaction, I would
> expect to see, in addition to the interaction, all main effects included in
> the interaction, i.e.
> posttest ~ group+pretest+pretest*Group
> John David Sorkin M.D., Ph.D.
> Professor of Medicine
> Chief, Biostatistics and Informatics
> University of Maryland School of Medicine Division of Gerontology and
> Geriatric Medicine
> Baltimore VA Medical Center
> 10 North Greene Street
> GRECC (BT/18/GR)
> Baltimore, MD 21201-1524
> (Phone) 410-605-7119
> (Fax) 410-605-7913 (Please call phone number above prior to faxing)
> *From:* R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> on
> behalf of Souheyla GHEBGHOUB <souheyla.ghebghoub using gmail.com>
> *Sent:* Sunday, April 21, 2019 4:57 AM
> *To:* René; r-sig-mixed-models using r-project.org
> *Subject:* [R-sig-ME] logistic regression on posttest (0, 1) with
> pretest(0, 1)*Group(Treatment, Ctrl) interaction
> Dear Rene, and any member of the list who is willing to read this : )
> I have decided to use the interesting model that you have structured for
> To refresh your memory here is what you said :
> *Anyway, there is a second possibility to define your model, depending on
> > how you want to interpret it. In the previous model you can say something
> > about the type-of-change likelihoods depending on the treatment group.
> > But you could implement the model as binomial as well (i.e. logistic
> > regression) mod2 <- brm(posttest ~ pretest*Group + (1|Subject) +
> > (1+Group|Word),...) And what you would expect here would be an
> > between pre-test and Group. For instance; if pretest=0 & treatment 1 then
> > posttest larger than with pretest=0 & treatment 2; but not when pretest=1
> > (because this is a plausible no gain situation). And so on...*
> But I found the interpretation of the Pretest*Group interaction results to
> be tough. I still can't grasp how to claim there is an effect of Group on
> posttest outcome when considering pretest. The pretest slope does not come
> with 0 or 1 in the output, the Group does come with one category, but its
> confusing what the intercept and slope estimates refer to in this case?
> Thank you very much,
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