Bioequivalence and Bioavailability Forum

Main page Policy/Terms of Use Abbreviations Latest Posts

 Log-in |  Register |  Search

Back to the forum  Query: 2018-03-24 22:18 CET (UTC+1h)

Two PK metrics: Inflation of the Type I Error [Two-Stage / GS Designs]

posted by Helmut Homepage - Vienna, Austria, 2017-11-12 11:57  - Posting: # 17971
Views: 1,386

Dear all,

related to this thread about dropouts. To run the R-code you need package Power2Stage 0.4.6+.

Let’s assume a CV of 25% for Cmax and 15% for AUC, Potvin ‘Method B’ (αadj 0.0294). We want to play it safe and plan the first stage like a fixed sample design (T/R 0.95, 80% power). Hence, we start with 28 subjects. In the interim the CVs are higher than expected; for Cmax a CV of 30% and for AUC 20%. Say Cmax is not BE (94.12% CI) and power <80%. Hence, we should initiate the second stage. Re-estimated sample size:

print(sampleN2.TOST(CV=0.30, n1=28), row.names=FALSE) # Cmax
# Design  alpha  CV theta0 theta1 theta2 n1 Sample size Achieved power Target power
#    2x2 0.0294 0.3   0.95    0.8   1.25 28          20      0.8177478          0.8

What does that mean? We would initiate the second stage with 20 subjects for Cmax but possible shouldn’t for AUC:

print(sampleN2.TOST(CV=0.20, n1=28), row.names=FALSE) # AUC
# Design  alpha  CV theta0 theta1 theta2 n1 Sample size Achieved power Target power
#    2x2 0.0294 0.2   0.95    0.8   1.25 28           0      0.8922371          0.8

Since the Type I Error strongly depends on the sample size, the study would be overrun for AUC and an inflated TIE is quite possible. If have no R-code yet to estimate how much… Suggestions are welcome.
I think that in the past everybody (including myself) looked only at the PK metric with the highest variability and ignored the other one. Likely not a good idea.

Which options do we have for the PK metric with the lower variability?
  1. Assess BE with a lower sample size. In the example above ignore the second stage entirely. If the CV would be 25% instead of 20%, assess only the first six subjects of the 20 in the second stage (i.e., in the pooled analysis 28+6=34 instead of 48).
  2. Use the data of all subjects and adjust α more (i.e., a wider CI). How?
  3. Or?
#1 would preserve the TIE but would regulators accept it (not using all available data)? What about #2, when the GL tells us that the α has to be pre-specified in the protocol?

Of course, this issue is not limited to TSDs but applies to GSDs with (blinded/unblinded) sample size re-estimation as well.

Helmut Schütz 

The quality of responses received is directly proportional to the quality of the question asked. ☼
Science Quotes

Complete thread:

Back to the forum Activity
 Mix view
Bioequivalence and Bioavailability Forum | Admin contact
18,076 Posts in 3,845 Threads, 1,139 registered users;
27 users online (0 registered, 27 guests).

It is better to be wrong than to be vague.
In trial and error, the error is the true essential.    Freeman Dyson

BEBAC Ing. Helmut Schütz