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Back to the forum  Query: 2017-11-23 19:32 CET (UTC+1h)
 

Russian «Экс­пер­тами» and their hobby [General Sta­tis­tics]

posted by Helmut Homepage - Vienna, Austria, 2017-04-29 00:46  - Posting: # 17278
Views: 3,984

Hi Artem,

concerning your question in the other thread:

» I need to calculate additional parameter in ANOVA - Cohort factor.

Oh, the hobby of the Russian «Экспертами»

» Then in the case of a EMA Model Specification is:
» sequence+subject(sequence)+period+treatment+cohort
» Am I right?

I’m afraid, no. The EMA does not specify a model. In the BE-GL we find only:

4.1.1 Study design
The study should be designed in such a way that the formulation effect can be distinguished from other effects.
4.1.8 Evaluation – Statistical analysis
The precise model to be used for the analysis should be pre-specified in the protocol. The statistical analysis should take into account sources of variation that can be reasonably assumed to have an effect on the response variable.


» And how Model Specification can be constructed for agencies recommending a mixed-effects model (FDA, Health Canada)?

You find the FDA’s models under the FOI and some members of the forum have a letter with the same wording. The FDA suggests three models (group instead of cohort):
  1. Group, Sequence, Treatment, Subject (nested within Group × Sequence), Period (nested within Group), Group-by-Sequence Interaction, Group-by-Treatment Interaction.
    Subject (nested within Group × Sequence) is a random effect and all other effects are fixed effects. Note that intra-subject contrasts for the estimation of the treatment effect (and hence, a PE and its CI) cannot be unbiased obtained from this model. It serves only as a decision tool.
    • If the Group-by-Treatment interaction test is not statistically significanta (p ≥0.1), only the Group-by-Treatment term can be dropped from the model. That means, pool the data and evaluate the study by model #2.
    • If the Group-by-Treatment interaction is statistically significanta (p <0.1), equivalence has to be demonstrated in one of the groups, provided that the group meets minimum requirements for a complete bioequivalence study. That means, no pooling and evaluate the (largest) group only by model #3.
  2. Group, Sequence, Treatment, Subject (nested within Group × Sequence), Period (nested within Group), Group-by-Sequence Interaction.
    Again, Subject (nested within Group × Sequence) is a random effect and all other effects are fixed effects.
    The model takes the multigroup nature of the study into account and is more conservative than the naïve pooled model (three degrees of freedom less than model #3).
  3. Sequence, Treatment, Period, Subject (nested within Sequence).
    Surprise: Subject (nested within Group × Sequence) is a random effect and all other effects are fixed effects.
However, the FDA also states that the simple model #3 (of pooled data) can be applied if all of the following criteria are met:I have no idea why the group effect is such a big deal in Russia. Practically the criteria for not using group terms is almost always fulfilled. The nasty thing is that the Group-by-Treatment interaction test has low power (therefore, testing at the 0.1 level). You should expect a false positive rate at the level of the test and trash some of your studies due to lacking power.b Bizarre.

Since Russia follows the EMA’s footprints, treat subjects as fixed instead of random.c The decision scheme (i.e., whether data can be pooled or analysis of the largest group is recommended) is applicable as well. It should be noted that in rare cases (e.g., extremely unbalanced sequences) the fixed effects model gives no solution and the mixed effects model has to be used.


  1. In Phoenix/WinNonlin check the Partial Tests for model #1:
    Column Hypothesis, row Group*Treatment and its P_value.
  2. Example: CV of AUC 30% (no scaling allowed) but 4-period full replicate to allow scaling of Cmax, GMR 0.90, target power 90% → sample size 54. Capacity of the clinical site 24 beds. Three options:
    1. Equal group sizes (3×18).
    2. Two groups with the maximum size (24) and the remaining one six.
    3. One group 24, the remaining two as balanced as possible (16|14).
    Let us assume that we are not allowed to pool (significant Group-by-Treatment interaction in model #1) and have to assess BE in the groups. Which powers can we expect?
    1. 51% in all groups (n=18 each).
    2. 62% in the two large groups (n=24 each).
    3. 62% in the largest group (n=24).
    Hence, I don’t think that equal group sizes in #1 are a good idea.
    #2 looks better but what if one group passes and the other not? If you cherry-pick and present only the passing one I bet that assessors will ask for the other one. What do you think they will conclude?
    Therefore, I would suggest #3…
  3. Setup of the models in Phoenix/WinNonlin (map Group as Classification):
    1. Group+Sequence+Treatment+Group*Sequence+
      Group*Period+Group*Treatment+Subject(Group*Sequence)

    2. Group+Sequence+Treatment+Group*Sequence+
      Group*Period+Subject(Group*Sequence)

    3. Sequence+Treatment+Period+Subject(Sequence)

[image]Regards,
Helmut Schütz 
[image]

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