Darborn ☆ Japan, 20240529 10:26 (20 d 07:54 ago) Posting: # 24007 Views: 1,101 

Hi, I got a problem related to the CV% (from a parallel BE) and confidence interval. The CV% from winnonlin is ~14% but confidence interval is 74%136%. Although the sample size for this trial is rather small (19 for analysis), the calculated CV% using confidence interval in PowerTOST is quite large (~39%). I'm not sure why the result is counterintuitive. Anyone have ideas? Sincerely, Jietian 
Helmut ★★★ Vienna, Austria, 20240529 11:39 (20 d 06:41 ago) @ Darborn Posting: # 24008 Views: 982 

Hi Jietian, ❝ I got a problem related to the CV% (from a parallel BE) and confidence interval. The CV% from winnonlin is ~14% but confidence interval is 74%136%. Although the sample size for this trial is rather small (19 for analysis), the calculated CV% using confidence interval in PowerTOST is quite large (~39%). I'm not sure why the result is counterintuitive. PowerTOST :
❝ Anyone have ideas? — Diftor heh smusma 🖖🏼 Довге життя Україна! _{} Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
Darborn ☆ Japan, 20240530 03:35 (19 d 14:45 ago) @ Helmut Posting: # 24009 Views: 909 

Hi Helmut, I can't upload files here, so feel free to download it from my onedrive share link. The version of winnonlin is 8.1. I used average bioequivalence with parallel design and formulation R as the reference. The only fixed effect is the Treatment (TRT) with Ln(X) transformation. No variance structure (left all empty). Everything else were left default (I think). Thanks! 
Helmut ★★★ Vienna, Austria, 20240530 11:21 (19 d 06:59 ago) @ Darborn Posting: # 24010 Views: 898 

Hi Jietian, mystery solved. Phoenix WinNonlin gives in Output Data → Final Variance Parameters the estimated variance and not the CV, which is for \(\small{\log_{e}\textsf{}}\)transformed data:$$\small{CV=100\sqrt{\exp (\widehat{s}^{2})1}}$$You have to setup a Custom Transformation in the Data Wizard :❝ I used average bioequivalence with parallel design and formulation R as the reference. The only fixed effect is the Treatment (TRT) with Ln(X) transformation. No variance structure (left all empty). However, you should not assume equal variances in a parallel design. This was not stated in the User’s Guide of WinNonlin 8.1. See the online User’s Guide of v8.3 for the setup. Forget the Levene pretest (which inflates the type I error) – always use this setup in the future. Then instead of the ttest the WelchSatterthwaitetest with approximate degrees of freedom is applied. For your complete data I got: While for C_{max} the CVs of T and R are quite similar, for AUC the one of R is more than twice the one of T. That’s because subject K015 had no concentrations after 48 h (which was also the C_{max}) and the AUC was way smaller than the ones of the other subjects.This has consequences for the BEcalculation since the degrees of freedom will be different as well. Therefore, the point estimates in both models are the same but the confidence intervals are not: It demonstrates why the ttest in case of unequal variances – and to a minor extent with unequal group sizes – is liberal (too narrow CI). Hence, in and SAS the Welchtest is the default.After excluding subject K015 I could confirm your results:
PowerTOST :
CI2CV() :The calculations are further based on a common variance of Test and Reference treatments in replicate crossover studies or parallel group study, respectively. — Diftor heh smusma 🖖🏼 Довге життя Україна! _{} Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
Helmut ★★★ Vienna, Austria, 20240530 13:08 (19 d 05:12 ago) @ Helmut Posting: # 24011 Views: 857 

Hi Jietian, reproducing your complete AUCresult in base:
Phoenix WinNonlin: — Diftor heh smusma 🖖🏼 Довге життя Україна! _{} Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
Darborn ☆ Japan, 20240531 04:07 (18 d 14:13 ago) @ Helmut Posting: # 24012 Views: 796 

Hi Helmut, I must give my whole appreciation to your reply. The detail was beyond my expectation. Thank you again for the explanation!!! Sincerely, Jietian Welcome! [Helmut] 