## BE study stratified per body weight group [General Sta­tis­tics]

Dear Martin,

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» Questions:
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» 1) Can the BE assessment be based on all data combined or does this require a separate analysis per body weight stratum
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» 2) If a combined assessment is the way to go how should the model look like
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» a) Ignoring body weight strata and use classical model (FDA): fixed effects for period, sequence, treatment and random effect for subject nested in sequence.
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» b) Including body weight strata as mentioned in ICH E9 (i.e. factors on which randomization has been stratified should be accounted for later in the analysis): fixed effects for period, sequence, treatment, body weight strata and random effect for subject nested in sequence.

If you will use a Proc GLM or lm() for the combined assessment you will face a confounding between subject effects and body-weight-strata effects and the analysis code will show you the finger. Something like df=0 will happen, at least for type III tests of effects.

You have to include at least a treatment by body-weight-strata interaction to get meaningfull results.
I'm not really sure if this confounding is also an issue if you plan to use Proc MIXED or lme()/lmer(). Make a example data set and try it.

I suggest that you modify the FDA code for logistic groups (see f.i. this post) accordingly (change group to body-weight-strata and drop Period(nested within Group)).
And read Helmut's lectures about "Multi-Group Studies in BE. To pool or not to pool?". All the criticism regarding the group effects apply also to body-weight-strata effects. The treatment effect (diff in the log domain, ratio on the original scale) in x-over studies is determined intra-subject. Thus all subject characteristics constant over the study can not influence it really.

If you are interested in having a look at BE assesment (ratio & CI) for the different body-weight-strata you have to go with 1) anyway.

BTW: "factors on which randomization has been stratified should be accounted for" should read the other way round: randomize stratified for factors planned to account for in the analysis.

Regards,

Detlew