Bioequivalence and Bioavailability Forum

Dear all,

I’m asking myself how to interpret this part of the BE-GL in the section Subject accountability:

Ideally, all treated subjects should be included in the statistical analysis. However, subjects in a crossover trial who do not provide evaluable data for both of the test and reference products […] should not be included.

Funny that the first sentence describes an “ideal” situation which can be handled only with a mixed effects model – which at least for conventional crossovers is taboo.
I would say that the second sentence was written having nonreplicated crossovers in mind. We discussed in the forum whether subjects with only RR-data (say due to dropouts in the 3^rd period of a partial replicate design in sequence RRT) should be included for the estimation of CV_wR and the consensus was: yes.
I simulated a small partial replicate with s_wT = s_wR = 0.3, s_wT = s_wR = 1 and removed the last observation of sub­ject 18 in sequence RRT:

subject period sequence treatment response
   1       1     TRR        T       75.76
   1       2     TRR        R       59.90
   1       3     TRR        R       91.15
   2       1     TRR        T       24.57
   2       2     TRR        R       19.52
   2       3     TRR        R       21.44
   3       1     TRR        T       53.38
   3       2     TRR        R       43.84
   3       3     TRR        R       32.97
   4       1     TRR        T      157.08
   4       2     TRR        R      115.90
   4       3     TRR        R      141.98
   5       1     TRR        T       76.29
   5       2     TRR        R      104.03
   5       3     TRR        R      169.86
   6       1     TRR        T       85.57
   6       2     TRR        R       49.44
   6       3     TRR        R       70.97
   7       1     RTR        R      453.94
   7       2     RTR        T      408.99
   7       3     RTR        R      452.39
   8       1     RTR        R      109.63
   8       2     RTR        T      128.59
   8       3     RTR        R      153.43
   9       1     RTR        R       83.15
   9       2     RTR        T       87.02
   9       3     RTR        R       62.62
  10       1     RTR        R       46.59
  10       2     RTR        T       26.13
  10       3     RTR        R       89.98
  11       1     RTR        R      131.53
  11       2     RTR        T      176.23
  11       3     RTR        R      124.95
  12       1     RTR        R       14.94
  12       2     RTR        T       13.03
  12       3     RTR        R       13.41
  13       1     RRT        R       75.16
  13       2     RRT        R       39.72
  13       3     RRT        T       94.14
  14       1     RRT        R      109.21
  14       2     RRT        R      131.66
  14       3     RRT        T      109.69
  15       1     RRT        R      121.48
  15       2     RRT        R      192.08
  15       3     RRT        T      143.01
  16       1     RRT        R      122.11
  16       2     RRT        R      127.09
  16       3     RRT        T      116.77
  17       1     RRT        R       59.09
  17       2     RRT        R       37.46
  17       3     RRT        T      105.95
  18       1     RRT        R      500.99
  18       2     RRT        R      155.44
  18       3     RRT        T       NA   

I compared Methods A, B, and C (yes!). If one uses the EMA’s code in SAS (or similar software) and clicks the button that’s the “all in” situation. Below that what happens if we exclude subject 18.

        Method  DF    CVwR    L      U       90% CI       PE     CI   GMR mixed   log-½ 
all in    A    32.0  30.52  79.71 125.46  92.47 125.36  107.67  pass pass  pass  0.15218
          B    32.0  30.52  79.71 125.46  92.13 124.89  107.27  pass pass  pass  0.15211
          C    15.6  29.74  80.00 125.00  90.89 125.91  106.98  fail pass  fail  0.16298
only TR   A    31.0  30.52  79.71 125.46  93.59 123.86  107.67  pass pass  pass  0.14008
          B    31.0  30.52  79.71 125.46  93.59 123.86  107.67  pass pass  pass  0.14008
          C    15.7  24.44  80.00 125.00  91.59 126.57  107.67  fail pass  fail  0.16174

The log half-width (log-½) is useful in comparison methods, where a higher value points to a more conservative decision (wider CI). As usual Method C is the most conservative one but not ”compatible with the guideline”. The Q&A states “… it will generally give wider [sic] confidence intervals than those produced by methods A and B.”. Applicants love it when an agency recommends a liberal method.
Further down in the Q&A:

For replicate designs the results from the two approaches [A and B] will differ if there are subjects included in the analysis who do not provide data for all treatment periods. Either approach is considered scientifically acceptable, but for regulatory consistency it is considered desirable to see the same type of analysis across all applications.
A simple linear mixed model, which assumes identical within-subject variability (Method B), may be acceptable as long as results obtained with the two methods do not lead to different regulatory decisions. However, in borderline cases and when there are many included subjects who only provide data for a subset of the treatment periods, additional analysis using method A might be required.

What does that mean? If we use only subjects with TR-data, results of A and B are identical. If we opt for “all in” an open question is what a “borderline case” is and “many included subjects who only provide data for a subset of the treatment periods” are.
Reading between the lines I got the impression that the EMA believes that Method A is always more conservative than Method B. This is not correct. Data set upon request.

What do you think? What do you do?