Bioequivalence and Bioavailability Forum

The power to know [Software]

posted by d_labes  – Berlin, Germany, 2008-05-08 13:16 (6205 d 11:30 ago) – Posting: # 1834
Views: 23,440

Dear Eva & Ohlbe & Helmut

❝ Since in my first post I showed identical results for SAS and WinNonlin, I would expect a coding issue in the SAS program you are using.

That is totally right and wrong.
The SAS Procedure GLM has the "standard" of treating all effects as fixed effects.
Thus if you use the code:
Proc GLM data=test;
  class treatment period sequence subject;
  model logAUC=treatment period sequence subject(sequence) /SS3;
Run;
you get Eva's result. This result is correct if all effects are considered fixed. That means regarding the subject effect we are making inference about the subjects which are actually under study.

But the goal of our BE study is to broaden the scope and make inference about some "population" of subjects (healthy volunteers f.i. from which we have randomly sampled our subjects actually under study).
This leads to subject as a random factor (effect) in our model.

The code in Proc GLM for that is:
Proc GLM data=test;
  class treatment period sequence subject;
  model logAUC=treatment period sequence subject(sequence);
  random subject(sequence) /test;
Run;
Proc GLM is resistant to our wishes and first displays the fixed effect analysis again (this is because according to SAS GLM handles random effects in a "post hoc manner", in direct words this feature was invented later and tinkered into GLM "mit heißer Nadel").

But then the sun is shining bright (emphasis by me):
The GLM Procedure
Tests of Hypotheses for Mixed Model Analysis of Variance

Dependent Variable: logAUC

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

Treatment           1      0.536468      0.536468     2.16    0.1918
Period              1      0.077422      0.077422     0.31    0.5966
Subject(Sequence)   6      1.573910      0.262318     1.06    0.4739
Error: MS(Error)    6      1.488671      0.248112

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

Sequence            1      0.610540      0.610540     2.33    0.1780
Error               6      1.573910      0.262318
Error: MS(Subject(Sequence))

Hope this helps a little bit in "the power to know".
SAS is an outstanding beast .

Side note: The annoying fixed effects analysis displayed again is not needed and can be suppressed using the "famous" ODS (whatever this is). Final code:
ODS exclude ExpectedMeanSquares ModelANOVA;
Proc GLM data=test;
  class treatment period sequence subject;
  model logAUC=treatment period sequence subject(sequence);
  random subject(sequence) /test;
Run;

Complete thread:

• SAS vs. WinNonlin: different sequence effect results Eva 2008-05-06 14:04 [Software] 
  • SAS vs. WinNonlin: different sequence effect results Ohlbe 2008-05-06 15:56
  • imbalanced design? Helmut 2008-05-06 16:13
    • Sample data & results Eva 2008-05-06 17:42
      • Error factor Ohlbe 2008-05-06 19:25
        • Error tests between/within Helmut 2008-05-06 21:33
          • Error tests between/within Helmut 2008-05-07 18:18
          • The power to knowd_labes 2008-05-08 11:16
            • The power to know Helmut 2008-05-08 17:18
              • The power to know d_labes 2008-05-09 09:35
                • The power to know Nirali 2008-05-09 11:00
                  • The power to know d_labes 2008-05-16 08:54
            • The power to know kevan 2009-05-25 15:46
              • Bogus statement for 2,2,2-BE ElMaestro 2009-05-25 22:25
                • Bogus? What? d_labes 2009-05-27 08:57
                  • Linear model on steroids ElMaestro 2009-05-28 19:12
              • Fixed nowadays what? d_labes 2009-05-27 09:03
          • Kinetica 5.0 bug Helmut 2008-12-31 16:42