-----Original Message-----
From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-project.org] On
Behalf Of Jorge Teixeira
Sent: Tuesday, 30 August, 2022 12:13
To: Phillip Alday
Cc: R-mixed models mailing list
Subject: Re: [R-sig-ME] time*treatment vs time + time:treatment in RCTs
Thank you, Philip.
1) would the model y (y1, y2 - but not y0) ~ y0 + time + time:treatment +
(1|ID) (*m3*) also violate the principle of marginality, in your opinion?
1.1) Would you still prefer m1 compared to m3?
*Final notes*:
Here is an interesting stata (sorry) about this:
https://stats.oarc.ucla.edu/stata/faq/what-happens-if-you-omit-the-main-effect-
in-a-regression-model-with-an-interaction/
Solomon's post.
https://solomonkurz.netlify.app/post/2022-06-13-just-use-multilevel-models-for-
your-pre-post-rct-data/
Thanks!
Phillip Alday <me at phillipalday.com> escreveu no dia segunda, 29/08/2022
?(s) 14:26:
On 8/29/22 05:53, Jorge Teixeira wrote:
Hi. In medicine's RCTs, with 3 or more time-points, whenever LMMs are
and the code is available, a variation of y ~ time*treatment + (1 | ID)
*(M1)* is always used (from what I have seen).
Recently I came across the model time + time:treatment + (1 | ID)* (M2)*
in Solomun Kurz's blog and in the book of Galecki (LMMs using R).
Questions:
*1)* Are there any modelling reasons for M2 to be less used in medicine's
RCTs?
It depends a bit on what `y` is: change from baseline or the 'raw'
measure. If it's the raw measure, then (M2) doesn't include a
description of differences at baseline between the groups.
Perhaps most importantly though: (M2) violates the principle of
marginality discussed e.g. in Venables' Exegeses on Linear Models
(https://www.stats.ox.ac.uk/pub/MASS3/Exegeses.pdf)
*2)* Can anyone explain, in layman terms, what is the estimand in M2? I
still struggle to understand what model is really measuring.
Approximately the same thing as M1, except that the "overall" effect of
treatment is assumed to be zero. "Overall" is a bit vague because it
depends on the contrast coding used for time and treatment.
You can see this for yourself. M1 can also be written as:
y ~ time + time:treatment + treatment + (1|ID).
If you force the coefficient on treatment to be zero, then you have M2.
*3)* On a general basis, in a RCT with 3 time points (baseline, 3-month
4-month), would you tend to gravitate more towards model 1 or 2?