sasikumar
☆    

Tamilnadu, India,
2008-05-31 13:30
(5780 d 17:33 ago)

Posting: # 1891
Views: 7,753
 

 P-Value with 90% Confidence Intervals [General Sta­tis­tics]

Dear All,

If I want to report PK parameters Cmax and AUC, why I have to use 90% CI and Why not P-Value?

If I use 90% CI, Cmax and AUC of test and reference formulation are bioequivalent. In this case, P-Value shows that test and reference formulation are significantly different.

Please clarify the above query.

Thanks and Regards,
S.Sasikumar
Jaime_R
★★  

Barcelona,
2008-05-31 15:04
(5780 d 15:59 ago)

@ sasikumar
Posting: # 1892
Views: 6,633
 

 BE # significance testing

Dear S.Sasikumar!

To quote the first paragraph of the forum's policy:

We expect a basic knowledge on BE/BA or related fields and the willingness to begin first with the Search function for similar problems.


In BE we are not interested in rejecting the null-hypothesis of equivalence (this is significance testing), but to reject the null-hypothesis of inequivalence. This can be done by performing two one sided t-tests at alpha 0.05 (the first one 'looking' whether T is below 80% of R and the second one 'looking' whether T is above 125% of R) - the sum of both p-values must be <0.10 to claim BE. Inclusion of a 90% confidence interval within the acceptance range is actually the same thing.
In any study of a high enough sample size you will get a statistically significant difference, but this has nothing to do with clinical significance (which is set mainly to 20%, or to 10% for NTI drugs in some regulations).
If your study was properly planned in terms of power and you get a significant treatment effect there are a couple of explanations possible:
  • lower intra-subject CV,
  • less difference of test from reference,
  • fewer drop-outs than expected, and
  • any combination of the above.
This is just a short blow; I leave the details to the statistical experts.

Edit:
See this post, and this post for references to textbooks.

Regards, Jaime
martin
★★  

Austria,
2008-05-31 18:30
(5780 d 12:33 ago)

@ sasikumar
Posting: # 1894
Views: 6,570
 

 P-Value with 90% Confidence Intervals

dear sasikumar!

this guideline may help in understanding the different concepts of testing for equivalence/non-inferiority and testing for difference/superiority: http://www.tga.gov.au/docs/pdf/euguide/ewp/048299en.pdf

testing for equivalence with a type I error of 5% can be done by the two-sided 90% confidence interval inclusion approach (similar to the two one-sided tests approach TOST). the calculated two-sided 90% confidence interval for the difference between test and reference treatment (e.g. ratio of geometric means) must fall completely within pre-specified margins of equivalence. conventional margins in pharmacokinetics are 0.8 to 1.25 which are symmetric margins for ratios as 1/0.8=1.25 and 1/1.25=0.8.

for more detail regarding testing for equivalence you may find this book helpful: Wellek S (2003). Testing Statistical Hypotheses of Equivalence. Chapman and Hall/CRC Press.

best regards

martin
Helmut
★★★
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Homepage
Vienna, Austria,
2008-06-04 17:41
(5776 d 13:22 ago)

@ martin
Posting: # 1918
Views: 6,539
 

 References again

Dear Martin!

http://www.tga.gov.au/docs/pdf/euguide/ewp/048299en.pdf


… is a really excellent reference for beginners!

❝ for more detail regarding testing for equivalence you may find this book helpful: Wellek S (2003). Testing Statistical Hypotheses of Equivalence. Chapman and Hall/CRC Press.


Oh wow, Wellek's book is not entry level!
I would recommend a selection from this post, namely IMHO increasing level:
Hauschke et al. (2007), Chow and Liu (2000), Patterson and Jones (2006), Senn (2000) – then it's time for Stefan Wellek… :ok:

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martin
★★  

Austria,
2008-06-04 20:33
(5776 d 10:30 ago)

@ Helmut
Posting: # 1919
Views: 6,442
 

 References again

dear hs !

thank your for pointing out that Wellek's book is too technical for beginners. for beginners I can recommend a document on the FDA's homepage (link). this document may also of interest to researchers with a basic understanding of this topic as this document addresses also the issue of multiplicity in the case of testing for equivalence in more than one parameter.

best regards

martin
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