BE study stratified per body weight group [General Sta­tis­tics]

posted by zizou – Plzeň, Czech Republic, 2018-06-08 23:11 (2002 d 18:28 ago) – Posting: # 18876
Views: 2,856

(edited by zizou on 2018-06-09 03:21)

Dear Martin,
additionally to what Helmut and Detlew wrote:

❝ 1) Can the BE assessment be based on all data combined or does this require a separate analysis per body weight stratum

Maybe easier way would be to separate the analysis as 2 studies:
- for subjects with weight <z kg,
- for subjects with weight >z kg.

Pro: standard evaluation (2x)
Con: approx. double number of subjects required (than in option 2))

❝ 2) If a combined assessment is the way to go how should the model look like ...

I would be inspired by FDA model with Groups (as Detlew pointed). Then it is an interesting question which factors should be included in the model:

Period                                  (1 or 2 or 3)
Sequence                                (six options acc. to Williams’ design)
BWS                                     (Body Weight Strata, values 1 or 2)
Subject (nested within BWS*Sequence)    (sequence and BWS are between-subject factors)
Treatment (nested within BWS)           (same situation as Period (nested within Group) in FDA suggested model for studies in multiple groups ... periods differ for different groups, here treatments differ for different BWS)

- The factor Treatment (nested within BWS) is THE END. It is not possible (for me) to evaluate the treatment difference as it is not the main effect but it is nested. When changed to main effect (i.e. just Treatment instead of Treatment(BWS) in the model) then we ignore the fact that treatment differs for BWS. (Wenn ich mich nicht irre.)

Possible option would be to use dose correction and then evaluation using the standard model.
Though I realized that there is the same dose correction for T1,T2,R in the half of the subjects and similarly in the second half ... so the ratios will stay unchanged regardless the correction.

Pro: Less subjects required in total (if you are not interested in separate analysis)
Con: model :confused:, varied questions may be arised

Best regards,

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