Dear Eraldo,
> When using fMRI for clinical purposes (eg; mapping hemispheric
> dominance for language function) one needs to compare the activations
> in a patient with the activations in a group of controls to test the
> reliability of any apparent difference in the patient
>
> This can be done as group x task interaction effect (one group has
> only one subject, the patient)
>
> My understanding is that fixed effects models would not be good here
> and a random effect analysis is needed
I think the usual qualifications apply here. If you want to infer that
the patient activates abnormally in relation to a specific group of
control subjects than a fixed-effects analysis is appropriate. If you
want to say that the abnormality is in relation to the population from
which the normals were selected, then a random-effects analysis is
called for. Both are valid.
The special caveat here is that the fixed effects analysis may be more
sensitive, which has implications in a clinical setting. The key
question here is which has the greatest predictive validity about
intervention outcomes.
> My understanding is also that a random effect analysis would come at
> the cost of the residual degrees of freedom with an issue on the
> magnitude of the controls sample size to achieve a sufficiently
> sensitive analysis
> Is there any estimate on how big should be the size of the control group?
Indeed - I would recommend at least 16 for a random effects analysis
and ideally 32.
With very best wishes,
Karl
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