Thanks again for the answer (y por el tiempo que me has dedicado en contestar a la pregunta, que no es poca cosa) :-)

Best regards

Javier]]>

Hola javier,

The purpose of your pilot is dual: To get an idea of variability so that your can dimension your pivotal trial appropriately, and to establish the time by which AUCt>0.8*AUCinf in the majority of (expected future) trial patients/volunteers.]]>

I couldnt agree more with you :PCchaos:

One more question,assuming that we will do the pilot trial, (in our case would be a healthy volunteer study because there is no safety concern about the drug)and the end of the day the question is the same right, How can we sure that que are taking enough sampling to cover the 80% of AUC-inf?

Forgive me for insisting but at heart i want to know how to do it just only for improving my kownlegde in pharmacology

Thanks in advance]]>

Hi javier,

This is

Biologics are much less defined that synthetic drugs, especially if they are produced by eukaryotic cells (S. cerevisiae etc). There are all sorts of little games going on with them which is not well understood and well controlled even in the most strictly regulated GMP facility. Check for example your batch release chromatograms (if you have them, if chromatolophystique is used where you work): You will see chromatograms that look like porcupines or someone having a really bad hair-day

-exactly which ones are active

-their exact structure

-how potent they are

-and which ones aren't potent/active.

All these species may have different properties in terms of ADME. You will likely see differences in apparent t½ for Test and Ref.

I recommend that you do a pilot trial and forget all about the info on the SPC. You are facing a source of error that is potentially much much higher than what you are used to from synthetics.

I think a pilot trial in a dedicated phase I facility is also the ethically correct thing to do.]]>

If anyone knows a guideline or PK book when this issue es explained it would be very helpful too

thanks in advance]]>

Hello

In my opinion Cmax is important BE studies if the RMP is administraded by specific route,(like subcutaneous) or has an clinical impact in this case mayors guideline like EMA and FDA demands it as a coprimary endpoints (toguether with AUC)

the regulatory authorithies demands a mean value for both product or demonstrade one primary endpoint is enough for bioequivalence (like intravenous infusion when F=100%)

I hope this explanation help you a little

best regards]]>

Helmut wrote:

"The inclusion of a 100(1–2α) confidence interval within the common acceptance range [L, U] of 80–125% is operationally equivalent to Two One-Sided t-Tests (TOST). One t-test is for ≤80% and the other one for ≥125%, both at a level of α 0.05."

Two tests (<80%, >125%), each with a 5% chance to fail, 5+5=10, gives an overall 10% chance to fail... Therefore 90% CI is equivalent to the TOST with 5% each.]]>

Dear David,

As

But based on your statement, it is 10% level of significance. Now I am in confusion :confused:... whether 5%level or 10% level of significance we need to consider for BE...? Please clarify.

Thanks.

Regards,

GM]]>

Dear Yura

Alpha is contained in the request to calculate 95% upper confidence limit of the linearized RSABE criterion. If you adjust alpha you have to calculate 1-alpha

`alpha=0.1`

to 2*alphaBut as already said: Nobody knows or can calculate the value of alpha

- Labes D, Schütz H.
*Inflation of Type I Error in the Evaluation of Scaled Average Bioequivalence, and a Method for its Control.*Pharm Res. 2016;33(11):2805–14. doi:10.1007/s11095-016-2006-1. full-text*view-only*

- Davit et al.
*"Implementation of a Reference-Scaled Average Bioequivalence Approach for Highly Variable Generic Drug Products by the US Food and Drug Administration"*

AAPS Journal, Vol. 14, No. 4, December 2012

Dear d_labes

So, is it right: for ABEL I should adjust Type I Error (alpha) or in other words to decrease to 0.025? What I can do if I’ll use RSABE (because here alpha is absent in formula)?]]>

Dear Yura,

don't get your point.

Can you please elaborate what you mean with "alpha change"?]]>

Dear David, Dear ElMaestro!

Totally correct.

The equivalence test is TOST (two-

to 1-2*alpha CI.

Difference test has a

The 95% CI of the above cited example --> 17193 is

95% CI = 89.86 ... 100.61% (contains 100%)

so no significant treatment effect, same conclusion as via the ANOVA p-value.]]>Hi Gm,

Your code seems ok to me.

In my opinion, your result is completly normal since the p-value is significant at the 10% significance level, which compares with the 90% CI with obtained. ElMaestro has a different opinion though, so you should follow our discussion :-D

Regards,

David]]>

Hi ElMaestro,

What I've said is that

The model is build the same way whether you are assessing difference in means or bioequivalence. The hypothesis are, however, different. For the hypothesis of "bioequivalence" the alpha level is 5%, and for the hypothesis of "difference in means" the alpha level is 10%. And the p-value

Hi David,

Here is the code used for my analysis.

`Proc GLM data=logdata;`

class Sequence Subject Period Form Cohort;

model Log(Param) = Sequence Period Form Cohort Form*Cohort Subject(Sequence*Cohort)/ SS3;

output out=outlier rstudent=student;

test h=Sequence e=Subject(Sequence*Cohort) / htype=3 etype=3;

lsmeans Form / pdiff CL alpha=0.10;

estimate 'A VS B' Form 1 -1;

run;

I got the values for CI is 77.27-98.78 (doesn't contain 100%) and p-value of treatment effect is 0.0709 (

My observation is that when removing the

`Form*Cohort`

term from the model, treatment effect is significant @5% level of significance.My question is that the terms which are used in the model are sufficient or not...? and if it is correct, why p-value of treatment effect is not significant...?:confused:

Thanks,

GM.]]>

Hi DavidManteigas and d_labes,

I beg to differ; the 90% CI approach applies a 5% alpha. A product which in not truly BE (GMR is 0.8 or below; or 1.25 or higher, can't be both), will have at most 5% chance of passing BE; te CI is made from 1.0-2alpha but that does not mean 10% chance of approving a non-BE product.

It is the same alpha 5% that is used in the ANOVA where the null hypo is sameness.

If I am wrong here then it is my very basic understanding of statistics that needs thorough remodeling.]]>

Hi d_labes and ElMaestro,

I'm also struggling with the question now. A 90% CI compares with a hypothesis test at 10%. The 90% CI is equivalent to a statistical assessment of equivalente at the 5% level due the TOST approach, since you're not assessing significance for the null hypothesis of difference in means. Nevertheless, when you apply the model the 90% CI interval is an interval for difference in means regardless of the interpretation of the results in the bioequivalence context. As the statistical conclusion of "difference in means" is obtained at the 10% level and not 5% level, and the term for formulation is assessing whether there is a "difference in means" and not equivalence, the p value for formulation will be significant at the 10% significance level if the 90% confidence interval does not contains 1. So it is completly plausible for me to have a 90% CI without 1 and a non-significant p value for formulation at the 5% significance level.

Am I understanding the issue wrongly?

Regards,

David]]>

Hi Martin,

A mixed model is only useful if you have more than 2 periods (or repeated measures). In the case you are explaining, if data is missing for one period it can't be used in any way regardless of the statistical method used.

Also, I can't understand why would you use a single sequence design in bioequivalence, and also why you use a mixed effects model in a "pre-post" design... Anyway, I guess that as long as you use a random slope and intercept model, residual variability will be your "within subject variability"... but not sure how that applies to a mixed effects model with two periods only.

Regards,

David]]>

Dear All

In "Draft Guidance on Progesterone" describes the calculation steps for HVD.

Tell me please, how to take into account of the alpha change when alpha is not present in the formulas?:-|

Best regards]]>

Dear Öberster Größter Meister,

:google: "duality confidence interval hypothesis testing" may help in understanding.]]>