Bioequivalence and Bioavailability Forum

Hello ElMaestro,

Thanks ElMaestro for your quick reply.

❝ Your proposal is perhaps not completely in line with model reduction habits in the pharmaceutical industry.

I am also confused about Anova model selection. This is a crossover study carried out in two groups of subjects(25 subjects in each group) within a month (e.g., if for logistical reasons only a limited number of subjects can be studied at one time), the statistical model should be modified to reflect the multigroup nature of the study. In particular, the model should reflect the fact that the periods for the first group are different from the periods for the second group.

I assume you mean the interactions or nestings?

- batch *treatment : It is the interaction effect.
- Period (batch): It is Period nested within Batch effect.
- Subject (sequence*Batch): it is Subject nested Within (sequence*Batch)effect.
- batch : It is Simply Batch effect.

❝ Let's take Subject(Sequence*batch); in this case imagine you have

❝

❝ -subject 1,2,3,4 in batch 1 of sequence 1.

❝ -subject 1,2,3,4 in batch 2 of sequence 2.

❝ -subject 1,2,3,4 in batch 1 of sequence 2.

❝ -subject 1,2,3,4 in batch 2 of sequence 1.

❝ All these subjects are different subjects, so they are just coded in a dumb way. It des not make sense to ask what the fixed effect of subject 3 is if you don't specify which of the four subject 3's you think of. In such a situation it makes sense to apply Subject(batch * sequence). The unreduced model matrix will have 16 columns for the fixed treatment levels for that (4+4+4+4 as indicated above) - you will then loose a couple of them due to the df redundancy. How many depends on whether you have requested an intercept and which factors are preceding Subject (batch * sequence) in your specification.

Sorry but, I did not get your explanation.