Outlier PK Study – Non Normal Distribution [Regulatives / Guidelines]
Good Morning
Recently we have conducted a Fasting PK study (single croosover). The statistical report is as follow:
As can be seen, the upper limit of the confidence interval do not meet PK criteria.
One of the subject is clearly an outlayer according Lund test. Removing the subject of the study, the results would be as follow:
resulting in meeting PK criteria, borderline, but meets criteria.
The thing is in my understanding it is not accepted to exlude a subject after the bioanalysis. How can we argue an outlier?
On the other hand, we have demostrate that the distribution of the RLD in case of CMax it is not normal. If we apply the arithmetic means (not transformed) to our subjects, the result is:
meeting perfectly the BE criteria (80 - 120%) even including the outlier.
Due the distribution is not normal, neither in RLD nor in Test, but extremely not normal in RLD, when we normalize the sequence the results show we are not meeting PK criteria because of the upper limit of the confidence interval of the CMax.
It is possible to arg the distribution RLD is non mormal to file the PK report with untransformed results?
Thanks for your help
Edit: Please don’t shout. Tabulators changed to spaces and BBcoded; see also this post #6. [Helmut]
Recently we have conducted a Fasting PK study (single croosover). The statistical report is as follow:
Dependent Ratio_%Ref_ CI_90_Lower CI_90_Upper CV%
Ln(Cmax) 118.34 103.66 135.11 37%
Ln(AUClast) 103.72 97.87 109.92 15%
As can be seen, the upper limit of the confidence interval do not meet PK criteria.
One of the subject is clearly an outlayer according Lund test. Removing the subject of the study, the results would be as follow:
Dependent Ratio_%Ref_ CI_90_Lower CI_90_Upper CV%
Ln(Cmax) 112.3365 101.6649 124.1284 29.36%
Ln(AUClast) 102.4207 96.91779 108.2361 14.98%
resulting in meeting PK criteria, borderline, but meets criteria.
The thing is in my understanding it is not accepted to exlude a subject after the bioanalysis. How can we argue an outlier?
On the other hand, we have demostrate that the distribution of the RLD in case of CMax it is not normal. If we apply the arithmetic means (not transformed) to our subjects, the result is:
Obs PK Mean_Ratio Lower_90 Upper_90 BE_Limits Result
1 AUCT 101.989 96.703 107.274 80-120 BE Untrans.
2 Cmax 93.634 83.252 104.016 80-120 BE Untrans
meeting perfectly the BE criteria (80 - 120%) even including the outlier.
Due the distribution is not normal, neither in RLD nor in Test, but extremely not normal in RLD, when we normalize the sequence the results show we are not meeting PK criteria because of the upper limit of the confidence interval of the CMax.
Obs PK Mean_Ratio Lower_90 Upper_90 BE_Limits Result
1 AUCT 101.989 96.703 107.274 80-120 BE untransformed
2 Cmax 93.634 83.252 104.016 80-120 BE untransformed
It is possible to arg the distribution RLD is non mormal to file the PK report with untransformed results?
Thanks for your help
Edit: Please don’t shout. Tabulators changed to spaces and BBcoded; see also this post #6. [Helmut]
Complete thread:
- Outlier PK Study – Non Normal DistributionDaLoe 2017-10-19 10:50 [Regulatives / Guidelines]
- Outlier PK Study – Non Normal Distribution jag009 2017-10-20 06:33
- Normal Distribution: Japan only Helmut 2017-10-23 10:25