[R] Validating a mixed-effects model

[Stephan Moratti]

Hi Armin,

Alternatively you could use premutation statistics. You could shuffle 
your subjects between groups randomly under the Null hypothesis of no 
differences between groups and each time claculating the lme model. I am 
not sure, but if you do it at each time point of your repetition at each 
draw, then you could remember the greates F value for your F-value 
distribution. This could control the multiple comparison problem. Then 
after let's say 1000 draws you have a F value distribution under the 
Null hypothesis and you could determine your critical F value from that 
distribution.

Hope that helps,

Stephan


Armin wrote:

Hi

I constructed a mixed-effects model from longitudinal repeated
measurements of lab values in 22 patients seperated into two groups
with the groups as fixed effect using lme. I thought about using the
jackknife procedure, i. e., removing any one subject and calculating
the fixed effect, to assess the stability of the fixed effect and
thereby validate the model. I suppose this has been done in the
following study:

http://content.nejm.org/cgi/content/full/357/19/1903
(this may be restricted access, sorry)

Is such an approach feasible?

Also in the article results are confirmed by comparing the mixed model
with a fitted least-squares regression. I understand that this can be
achieved with lmlist, but only for for models without an additional
fixed effect!?

Are there any other good approaches to validate a mixed-effects model
that will be accepted in medical peer review?


-- 
*Stephan Moratti, PhD/
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