Significant ≠ relevant [Design Issues]

posted by zizou – Plzeň, Czech Republic, 2016-03-25 22:41 (2952 d 16:47 ago) – Posting: # 16141
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(edited by zizou on 2016-03-26 00:47)

Hi everybody and nobody.

❝ And the second is when there were drop-outs in the study and subjects were replaced by doubles


Personally I don't like the option of replacing of subjects. But some sponsors require that.
For example 26 subjects are needed for 80% power. Expected drop-out rate xx%. Sample size 32. When number of subjects for BE evaluation is lower than 26 subjects, additional subjects will be treated.
Ok. In my poor practice I was lucky and never got such "grouped" data for evaluation x). Nevertheless I am afraid of that: I will have results from 25 subjects and from another 1 subject (formerly alternate). Then "group" effect can be significant purely by chance. Of course the group effect (after possible replacing of subjects) is not mentioned in protocol, but if it becomes required to test it. ...pfff... sponsor just looses money by treating 1 subject separately when only the larger group of 25 subjects will be evaluated in the final (if group effect tested with significant result - not expected of course).
Not mentioning about the option that 26 subjects will complete clinical part. Bioassay will be processed. Then pharmacokineticist gets the concentrations and he figures out that one subject has pre-dose concentration in period 2 higher than 5% of corresponding Cmax. Then almost all has been done and according to the protocol 1 subject (one of alternates) has to come in the clinical part. GRRR! (not expected as well)

❝ Can we perform 2 different ANOVA models: first to exclude group effect, and second - to make a standard treatm+period+seq+sub(seq) calcaulation?


It seems Ok. for me. However it should be stated in protocol..., when it is after deficiency letter, it should be stated in that letter :-D .

❝ What if we do get significant group term? Can we somehow make sure that number of subjects from only one group is sufficient for the study? What else can we take in such a case?


You can calculate post-hoc power (sometimes required by regulatory when sample size was calculated with assumptions "GMR in 0.95-1.05 and intra-subject CV x%" and true GMR was outside the expected interval or intra-subject CV was higher than expected. When the results are better than expected it could be possible to show that number of subjects from only one group is sufficient (especially when only one replaced subject was in the second group).

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