Bioequivalence and Bioavailability Forum

Dear comrades-in-arms!

❝ Nützt nüchts. Ich werde's nie verstehen. Ist auch scheissegal.

Yep. Let me throw in Phoenix6.2 in the spirit of EMA’s Q&A p25:

‘The analysis presented above show that this approach (Method A) is feasible even for unbalanced replicate design studies. The advantage of this approach is that it is straightforward and that it appears to be software and software option independent.’

(my emphases)

Sequential Tests
Hypothesis        Numer_DF  Denom_DF       F_stat     P_value
Period                   3        71       2.6252933  0.05702846
Sequence                 0  Not estimable
Sequence*Subject         75       71       7.9380219  0

Partial Tests
Hypothesis        Numer_DF  Denom_DF       F_stat     P_value
Period                   3        71       1.3322478  0.27074552
Sequence                 0  Not estimable
Sequence*Subject         75       71       7.9380219  0

‘Sequential Tests’ in Phoenix/WinNonlin = SAS I
Quote from the manual about ‘Partial Tests’:

The partial tests in LinMix are not equivalent to the Type III method in SAS though they coincide in most situations.
The Partial Tests worksheet is created by testing each model term given every other model term. Unlike sequential tests, partial tests are invariant under the order in which model terms are listed in the Fixed Effects tab. Partial tests factor out of each model term the contribution attributable to the remaining model terms.
This is computed by modifying the basis created by the QR factorization to yield a basis that more closely resembles that found in balanced data.
For fixed effects models, certain properties can be stated for the two types of ANOVA. For the sequential ANOVA, the sums of squares are statistically independent. Also, the sum of the individual sums of squares is equal to the model sum of squares; which means the ANOVA represents a partitioning of the model sum of squares. However, some terms in ANOVA may be contaminated with undesired effects. The partial ANOVA is designed to eliminate the contamination problem, but the sums of squares are correlated and do not add up to the model sums of squares. The mixed effects tests have similar properties.

Aha!