[R-sig-ME] logistic regression on posttest (0, 1) with pretest(0, 1)*Group(Treatment, Ctrl) interaction

[Souheyla GHEBGHOUB]

Dear John,

Thank you for the sound advice I will make sure to always be explicit when
writing  models.
Thank you and I totally understand you cannot be of help in this specific
post.

Kind regards,
Souheyla

On Sun, 21 Apr 2019 at 19:55, Sorkin, John <jsorkin using som.umaryland.edu>
wrote:

> Souheyla,
>
> Souheyla,
>
> I don't know every R package so I can't make a blanket statement.
> Searching the internet makes it appear that  using lm(y~x*z) will produce
> a model with the main effects and the interaction, i.e. y=f(x,z,x*z).
> Nevertheless, I believe it to be best coding practices (especially when
> sending your code to a third part) to always expressly give all terms in a
> model and never leave the main effects out of a model with an interaction.
> I am sorry I can't be of any additional help with the more fundamental
> aspects of your original posting.
>
> John
>
> 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:* Souheyla GHEBGHOUB <souheyla.ghebghoub using gmail.com>
> *Sent:* Sunday, April 21, 2019 2:07 PM
> *To:* Sorkin, John
> *Cc:* René; r-sig-mixed-models using r-project.org
> *Subject:* Re: [R-sig-ME] logistic regression on posttest (0, 1) with
> pretest(0, 1)*Group(Treatment, Ctrl) interaction
>
> Dear John,
>
> 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,
> Souheyla
>
>
> On Sun, 21 Apr 2019 at 19:02, Sorkin, John <jsorkin using som.umaryland.edu>
> wrote:
>
> Souheyla,
>
> 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
>
> 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
> me,
> 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
> interaction
> > 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,
> Souheyla
>
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