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Subtleties, flaws, questions [BE/BA News]

posted by d_labes - Berlin, Germany, 2011-03-22 09:24  - Posting: # 6794
Views: 24,539

Dear Helmut!

» ... but I'm wondering how EMA got their results for 'Method C' with this statement: estimate 'test-ref' formulation -1 1/ CL alpha=0.10;

This is one of the hidden gems in SAS coding :-D. But it is correct if you use the codes 'R' and 'T' for the formulations. SAS orders them lexically 'R' coming first and expects the coefficients for the difference in the estimate statement in that order.

» @ Priyanka:
» ... ADJUST=T is not stated by FDA (2001), EMA's Q&A (2011), and FDA's Progesterone Guidance (2010, 2011).

Here the Great Admin err's. It is stated in EMA's Q&A Method 1:
proc glm data=replicate;
  class formulation subject period sequence;
  model logDATA= sequence subject_(sequence) period formulation;
  *the space above may lead to hard to discover errors!;
  estimate "test-ref" formulation -1+1;
  test h=sequence e=subject(sequence);
  lsmeans formulation / adjust=t pdiff=control("R") CL alpha=0.10;

But as I said above, it is not necessary here. Also the pdiff=control("R") is not necessary. But it does here the job of ordering T first and thus giving the LSMeans difference T-R because again the LSMean of 'R' comes first and the difference 1-2 is calculated.
Moreover the whole lsmeans statement is superfluous if you code the option /CLparm alpha=0.1 in the model statement. Then the estimate statement will give the CI like Proc MIXED does as default.

BTW: I'm not sure if the test of the sequence effect as coded from Great Oracle EMA is appropriate in case of missings. Using the 'Capt'n EM calls me bogus' Random statement I get a mixture of MSerror and MSsubject(sequence) as denominator of the corresponding F-test. The degrees of freedom are also adapted according to Satterthwaite.

BTW2: The code for obtaining the intra-subject variance taken literally will bring us directly to the Type III hell. Output (I have named their DATA as AUC):
--- GLM-ANOVA Analysis of REF. within-subject var. for log(AUC) ---

The GLM Procedure

Dependent Variable: logval   log(AUC)

                                Sum of
Source                DF       Squares    Mean Square   F Value   Pr > F

Model                 78   120.2314511      1.5414289      7.73   <.0001
Error                 71    14.1512621      0.1993136
Corrected Total      149   134.3827132

Source                DF     Type I SS    Mean Square   F Value   Pr > F

sequence               1     0.0880117      0.0880117      0.44   0.5085
subject(sequence)     75   119.3707667      1.5916102      7.99   <.0001
period                 2     0.7726727      0.3863363      1.94   0.1515

Source                DF   Type III SS    Mean Square   F Value   Pr > F

sequence               0     0.0000000       .              .      .
subject(sequence)     75   118.6616824      1.5821558      7.94   <.0001
period                 2     0.7726727      0.3863363      1.94   0.1515

As you see: Subtleties and flaws in coding, questions over questions after that 'Clarification'.
But what could we expect other if considering the scientific foundation of that all?



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