## Russian «Экспертами» and their hobby [General Statistics]

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

» 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):

Since Russia follows the EMA’s footprints, treat subjects as fixed instead of random.

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):

- 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 significant
^{a}(*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 significant
^{a}(*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.

- If the Group-by-Treatment interaction test is not statistically significant
- 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).

- Sequence, Treatment, Period, Subject (nested within Sequence).

Surprise: Subject (nested within Group × Sequence) is a random effect and all other effects are fixed effects.

*pooled*data) can be applied if*all*of the following criteria are met:- the clinical study takes place at one site,

- all study subjects have been recruited from the same enrollment pool,

- all of the subjects have similar demographics, and

- all enrolled subjects are randomly assigned to treatment groups at study outset.

*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.- In Phoenix/WinNonlin check the
`Partial Tests`

for model #1:

Column`Hypothesis`

, row`Group*Treatment`

and its`P_value`

.

- Example: CV of AUC 30% (no scaling allowed) but 4-period full replicate to allow scaling of C
_{max}, GMR 0.90, target power 90% → sample size 54. Capacity of the clinical site 24 beds. Three options:- Equal group sizes (3×18).

- Two groups with the maximum size (24) and the remaining one six.

- One group 24, the remaining two as balanced as possible (16|14).

- 51% in all groups (n=18 each).

- 62% in the two large groups (n=24 each).

- 62% in the largest group (n=24).

#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… - Equal group sizes (3×18).
- Setup of the models in Phoenix/WinNonlin (map
`Group`

as`Classification`

):`Group+Sequence+Treatment+Group*Sequence+`

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

`Group+Sequence+Treatment+Group*Sequence+`

Group*Period+Subject(Group*Sequence)

`Sequence+Treatment+Period+Subject(Sequence)`

—

All the best,

Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. ☼

Science Quotes

All the best,

Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. ☼

Science Quotes

### Complete thread:

- Russian «Экспертами» and their hobby - Helmut, 2017-04-29 00:46 [General Statistics]
- Low power of Group-by-Treatment interaction - mittyri, 2017-04-29 22:57
- Let’s forget the Group-by-Treatment interaction, please! - Helmut, 2017-04-30 13:54
- Let’s forget the Group-by-Treatment interaction, please! - ElMaestro, 2017-05-01 16:19
- Some answers - Helmut, 2017-05-02 01:10
- Some answers - ElMaestro, 2017-05-02 09:04
- Example - Helmut, 2017-05-02 12:35

- Sensitivity of term? - mittyri, 2017-05-02 18:29
- Simulations - Helmut, 2017-05-05 14:38
- loosing specificity due to low sensitivity - mittyri, 2017-05-08 23:28
- loosing specificity due to low sensitivity - Helmut, 2017-05-09 00:55

- loosing specificity due to low sensitivity - mittyri, 2017-05-08 23:28
- Loss in power - Helmut, 2017-05-06 17:31
- Interval between groups - Helmut, 2017-05-08 19:02
- IMP handling - mittyri, 2017-05-08 23:40
- IMP handling - Helmut, 2017-05-09 01:08

- IMP handling - mittyri, 2017-05-08 23:40
- Loss in power - Helmut, 2017-05-14 17:22

- Simulations - Helmut, 2017-05-05 14:38

- Some answers - ElMaestro, 2017-05-02 09:04
- No convergence in JMP and Phoenix WinNonlin - Helmut, 2017-05-25 15:26
- Ouch?!??? - ElMaestro, 2017-05-25 16:24

- Some answers - Helmut, 2017-05-02 01:10

- Let’s forget the Group-by-Treatment interaction, please! - ElMaestro, 2017-05-01 16:19

- Let’s forget the Group-by-Treatment interaction, please! - Helmut, 2017-04-30 13:54
- Russian «Экспертами» and their hobby - Artem Gusev, 2017-05-02 16:13
- be careful with mixed models - mittyri, 2017-05-02 17:53
- be careful with mixed models - Artem Gusev, 2017-05-03 11:02

- p-value(s) in model 2 - Helmut, 2017-05-05 14:48

- be careful with mixed models - mittyri, 2017-05-02 17:53
- Russian «Экспертами» following the EEU GLs - Helmut, 2017-05-24 20:17
- Russian «Экспертами» following the EEU GLs - Beholder, 2017-05-24 22:37
- Penalty for carelessness - mittyri, 2017-05-25 08:52
- Russian «Экспертами» following the EEU GLs - Beholder, 2017-05-25 10:43

- Russian «Экспертами» following the EEU GLs - Beholder, 2017-05-24 22:37

- Low power of Group-by-Treatment interaction - mittyri, 2017-04-29 22:57