Results [General Sta­tis­tics]

posted by randombadger – UK, 2011-05-03 14:03 (5121 d 09:12 ago) – Posting: # 6982
Views: 11,184

Dear all,

Thank you for your responses so far, they have been very supportive. Please see below for results from my analyses:

Model 1
PROC MIXED DATA= cmax ;
   CLASS USUBJID period treat ARMCD ;
   MODEL LOG_PARM = treat period ARMCD  /ddfm=KR; 
   RANDOM USUBJID(ARMCD) /G;
   REPEATED/GRP=treat SUB=usubjid ;
   ESTIMATE "B vs A"  TREAT  -1 1 /cl alpha=0.1;
   LSMEANS treat/ cl alpha=0.1;
RUN;

Estimated G Matrix

                                                Planned
                        Unique Subject          Arm
   Row  Effect          Identifier              Code    Col1     Col2

     1  USUBJID(ARMCD)  XXX XXX-X-XXX-00011001  ABB     0.07056     
     2  USUBJID(ARMCD)  XXX XXX-X-XXX-00011003  ABB             0.07056


(block diagonal, apologies for the lack of formatting)
etc

Intra-Subject
CV A (%)    CV B (%)
 31.98       10.02


Model 2
PROC MIXED DATA= cmax ;
  CLASSES armcd usubjid period treat ;
  MODEL LOG_PARM = armcd period treat / DDFM=KR;
  RANDOM treat/TYPE=FA0(2) SUB=usubjid G;
  REPEATED/GRP=treat SUB=usubjid;
  ESTIMATE 'B vs A' treat -1 1/CL ALPHA=0.1;
  LSMEANS treat/ cl alpha=0.1;
RUN;


Estimated G Matrix
                 Unique Subject
Row    Effect    Identifier            TREAT    Col1     Col2

1    TREAT     XXX XXX-X-XXX-00011001    A     0.1036   0.05780
2    TREAT     XXX XXX-X-XXX-00011001    B     0.05780  0.07110


Intra-Subject
CV A (%)    CV B (%)
 22.88        9.78


As you can see, the differences in CV's are quite large hence my concern on the most appropriate code to apply. Note: the treatments were nowhere near bioequivalent.


Edit: Formatted. You may use BBCodes (see here). [Helmut]

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