Joakim Englund <joakim.englund@GMAIL.COM> wrote
>I'm currently learning about general strategies of how to select the "best"
>covariance structure in repeated measurements trials using proc mixed. To
>aid me I use "Applied Mixed Models in Medicine" by Helen Brown and Robin
>Prescott. They propose to use a likelihood ratio test when choosing between
>two different models. This seems reasonable to me and can be used whenever
>the simpler model is nested within the more complex one.
As an aside - is the book good?
>Question 1: Do you agree with this approach, or would you prefer to use
>criterias such as AICC which penalises over-parameterised models? (I know
>one mussed take other factors into consideration, such as trial design and
>the meaning of the structure, but I would like your opinion on this
>technical approach from an "all other things being equal" - viewpoint.)
>If one chooses to proceed with the likelihood ratio test approach, there can
>be situations when the choice of structure stands between two models where
>no one is nested within the other. In such an instance, there is no formal
>test that can be applied. Brown and Prescott then suggests to pick the model
>with the highest likelihood (even though that approach is not explicitly
I await the response of Dale (and others) who really know this stuff cold. But I tend
to favor AICC in general. In my experience, though, AICC, AIC and LR (where applicable) all
give the same answer. I know this isn't mathematically necessary, but that's what I've nearly
I'd be interested in other people's experience.
>Question 2: In the situation described above, would it not be more
>appropriate to use something like AICC, as looking merely at the likelihood
>would always favour more parameterised models?
Clearly you can't use the LR test here.
There's debate among advocates of AIC, AICC, SBC and others, about which is best. I haven't kept
up with this debate.
I look forward to discussion of this topic!
Peter L. Flom, PhD
www DOT peterflomconsulting DOT com