Hi LMR
If the interaction term is not statistically significant then there is no evidence of the existence of two different groups in your sample (as far as the longitudinal trajectory is concerned they are all controls, the groups might be different at baseline though). This is why main effects are only tested after the interactions have been previously tested. In your model a common “base time slope” is assumed for both groups (the second coefficient) but you are also explicitly modeling the possibility of the case-group slope being exceeding the control/common base slope by an extra quantity. That quantity is the interaction term.
Hope this makes sense
Best -Jorge
De: Lars M. Rimol larilin@gmail.com Para: FS maling list freesurfer@nmr.mgh.harvard.edu Enviado: Miércoles 18 de junio de 2014 8:57 Asunto: Re: [Freesurfer] Linear Mixed Models in FS?
Hi Jorge,
Thank you for your reply!
Again considering the same model from before
intercept(random
effect) + centered age + group + group x centered age + sex
I think what is confusing me is that I think of the [centered age]
covariate as a column vector which will contain the centered age of both the control- and the case group. This is how it would be seen in a GLM using the same design matrix. Therefore it is difficult for me to understand how the contrast [0 1 0 0 0] can inform us about the control group alone. To me it would seem obvious that this contrast tells me something about the effect of [centered age] on the whole of the sample, regardless of the group each subject belongs to.
On the other hand, I agree with you that the interaction term could
tell us something about the effect of [centered age] on the case-group by considering the contrast vector [0 0 0 1 0].
Just for the sake of argument, please consider the following model
intercept(random
effect) + (1-group) x centered age + group + group x centered age + sex
and compare to the one presented above. Here (1-group) is a column vector
which is 1 where the [group] vector is 0, and vice versa. This difference ensures that the second term only includes numbers from the control-group. Applying the contrast [0 1 0 0 0] to this model, would this not be more appropriate for consider the effect of [centered age] on the control-group alone?
Given your previous answers I
suspect I'm missing something here, but I would greatly appreciate if you could please take the time to explain to me how I've gone wrong.
Thanks! LMR
Hi LMR
- Yes, you should
use n-1 (0/1) covariates to model n groups. Eg. (Controls, Case 1 and Case 2) the model would be:
intercept(random effect) + centered age?(might be a random effect too)?+ ?Case1 + Case1 x centered age + Case2 + Case2 x centered age + sex
2)In model:
intercept(random effect) + centered age + group + group x centered age + sex
the fourth coefficient is the interaction term that represents the difference in slope between the patient and control groups. This is easy to see from your Question 1 equations. It's also easy to see from those equations that [0 1 0 0 0] tests the effect of time in the control group since the group-specific slope is only equal to the coefficient of the time covariate (the second covariate) when the group covariate is zero (i.e for the controls).
Hope this makes sense.
Best
-Jorge
yours,
Lars M. Rimol, PhD St. Olavs Hospital Trondheim, Norway _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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