Intercept in mixed effects model [Software]
Hi Imph,
The choice is yours, really. The very few textbooks or papers that I know of, which specify a
model for BE (i.e. with an equation, not with verbal mention of effects) seem to give it with an intercept term for fixed effects, but even that can be debated depending on your purpose.
If you need to extract a difference in treatment effects (and in BE you always do), then it is much more straightforward to simply fit the model without intercept and keep treatment as the first effect. You get what you want right off the bat from the first two elements of the model effects vector. It is not necessary to understand all aspects of regressors and contrast coding to make easy use of it.
The choice does not affect the model residual, so the 90% CI is the same. Or you can take the long way around the problem and extract LSMeans (e.g. package emmeans in R). This option is fine, too, and it works regardless of whether the model was fit with an intercept or not. What goes on behind the curtains is complicated, to say the least, and I shall not claim I understand it too well. Quite comforting to me was when another user on this forum tried to work out LSMeans "by hand" and got something unexpected. I didn't feel so hopeless after reading that post.
Intercepts on the random effects are a default trait with some software packages. Random terms with an intercept term is a cosmic mindf#cker, simply because I am used to directly thinking of a covariance matrix with only variance terms. Easier in my head for me.
But it is fully valid when specified with the intercept, too. It does not provide better convergence properties, so perhaps habits, taste and software defaults prevail here.
❝ To assess average bioequivalence based on a mixed effect model in Phoenix WinNonlin, do we have to include the intercept term for the fixed effects and the random intercept for the random effects? does the analysis change with the inclusion or non-inclusion of the intercept term.
The choice is yours, really. The very few textbooks or papers that I know of, which specify a
model for BE (i.e. with an equation, not with verbal mention of effects) seem to give it with an intercept term for fixed effects, but even that can be debated depending on your purpose.
If you need to extract a difference in treatment effects (and in BE you always do), then it is much more straightforward to simply fit the model without intercept and keep treatment as the first effect. You get what you want right off the bat from the first two elements of the model effects vector. It is not necessary to understand all aspects of regressors and contrast coding to make easy use of it.
The choice does not affect the model residual, so the 90% CI is the same. Or you can take the long way around the problem and extract LSMeans (e.g. package emmeans in R). This option is fine, too, and it works regardless of whether the model was fit with an intercept or not. What goes on behind the curtains is complicated, to say the least, and I shall not claim I understand it too well. Quite comforting to me was when another user on this forum tried to work out LSMeans "by hand" and got something unexpected. I didn't feel so hopeless after reading that post.
Intercepts on the random effects are a default trait with some software packages. Random terms with an intercept term is a cosmic mindf#cker, simply because I am used to directly thinking of a covariance matrix with only variance terms. Easier in my head for me.
But it is fully valid when specified with the intercept, too. It does not provide better convergence properties, so perhaps habits, taste and software defaults prevail here.
—
Pass or fail!
ElMaestro
Pass or fail!
ElMaestro
Complete thread:
- Intercept in mixed effects model Imph 2022-10-25 09:47 [Software]
- Intercept in mixed effects model Helmut 2022-10-25 11:09
- Intercept in mixed effects modelElMaestro 2022-10-31 17:46
- Intercept in mixed effects model PharmCat 2022-11-03 15:02
- More obvious to discuss the model.... for me... ElMaestro 2022-11-10 00:34