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Hi,
I would like to hear from you about conducting a BE study with 48
volunteers (2x2, crossover, randomized, fed, open, 14-day washout)
splitting the volunteers into 2 groups (24 for Group A and 24 for
Group B) as follow:
- Day 1: First confinement for Group A (Period 1)
- Day 7: First confinement for Group B (Period 1)
- Day 14: Second confinement for Group A (Period 2)
- Day 21: Second confinement for Group B (Period 2)
This procedure (split the volunteers into 2 groups) should be done
because the numbers of beds into the clinical facility (only 24 beds
is available) does not allow to confine all of them at the same time.
Questions:
- Is there any regulatory issue about doing the confinement in this way?
- Do I need to consider this situation into the statistical analysis?
Any comments will be welcome ...
Regards,
Rafael E. Barrientos Astigarraga
MAGABI Pesq. Clin. Farm. Ltda.
rafael.barrientos.aaa.magabi.com.br
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The following message was posted to: PharmPK
Hi Rafael,
You need to add an identifier for the group. We usually use date of dose
(a variable already in our dataset) to the ANOVA.
No regulatory issue (for US anyway) if you account for the 2 groups.
Susan
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I'd test for a group effect by accounting for the design in the
statistical analysis. You'd first have to use a pooled model to test for
a group effect (group*treat). If there is no group effect, you can
remove the group*treat factor from the model. If there is a group
effect, then you'd need to analyze the data from the two groups
separately. In the latter case, BE assessment will be based on the two
individual models.
Hope this makes sense!
Regards,
Nav Coelho
Senior Biostatistician, Biovail Contract Research
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Hi,
- Is there any regulatory issue about doing the confinement in this way?
No there will be no issue from the regulatory side. But you have to
do the Stat analysis after
the whole study is completed.
- Do I need to consider this situation into the statistical analysis?
Yes you have to consider this situation into your statistical analysis
ANOVA model by introducing Group effect.
If you want in detail what are the factors in the ANOVA Model, please
see the below link for detailed description:
http://forum.bebac.at/mix_entry.php?id=834#p1330
--
Regards,
M.V.G.S.Raghavendra
Biostatistician
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The following message was posted to: PharmPK
Dear Rafael,
your design seems to be reasonable.
> - Is there any regulatory issue about doing the confinement in this
way?
>
I don't think so.
> - Do I need to consider this situation into the statistical analysis?
US-FDA states (
http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm070244.pdf
, Section VII.A.):
If a crossover study is carried out in two or more groups of subjects
(e.g., if for logistical reasons only a limited number of subjects can
be studied at one time), the statistical model should be modified to
reflect the multigroup nature of the study. In particular, the model
should reflect the fact that the periods for the first group are
different from the periods for the second group. This applies to all of
the approaches (average, population, and individual BE) described in
this guidance. [...] If the study is carried out in two or more groups
and those groups are studied at different clinical sites, or at the same
site but greatly separated in time (months apart, for example),
questions may arise as to whether the results from the several groups
should be combined in a single analysis. Such cases should be discussed
with the appropriate CDER review division.
EU-EMEA states (
http://www.emea.europa.eu/pdfs/human/qwp/140198enfin.pdf
, Section 3.1):
The study should be designed in such a way that the formulation effect
can be distinguished from other effects.
The same wording is used in the drafted guideline
(
http://www.emea.europa.eu/pdfs/human/qwp/140198enrev1.pdf
, Section 4.1.1).
In the recent past I had to deal with deficiency letters from the Gulf
States (Saudia Arabia, Emirates), where inclusion of a group-factor in
statistical analyses was requested.
Most people simply ignore a potential group factor if the same protocol
is used and groups are not 'to far apart' (in statistical terms 'naA've
pooling' is applied). Others include a group factor as a fixed effect in
the statistical analysis (additional to 'sequence', 'treatment',
'period'). Group is a between-subject factor, therefore between-subject
variability should be used as error-term. Some people additionally add a
fixed 'treatment x group' interaction factor as well. I would not
suggest this nested term, because it is unclear which error term should
be used in the test (treatment is a within-subject factor and group a
between-subject factor).
Good luck,
Helmut
P.S.: Due to the redesign of FDA's website almost all documents were
moved to new locations (no automatic URL-redirects are in place;
professionals at work...). For an overview of some regulatory documents
see here:
http://bebac.at/news/2009-06-12.htm
-
Ing. Helmut SchAtz
BEBAC - Consultancy Services for
Bioequivalence and Bioavailability Studies
Neubaugasse 36/11
1070 Vienna, Austria
e-mail helmut.schuetz.at.bebac.at
web http://bebac.at/
contact http://bebac.at/Contact.htm
forum http://forum.bebac.at/
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Dear All,
First of all thanks to all for such a richest information about BE
studies in groups. i would appreciate if any get me update me about
the Group effect is mandatory or not to consider in statistical
analysis & How many days difference between confinement of two groups
is relevant if there is any supportive article or guidance, then
Kindly post the same.
thnaks in advance
Regrads
Harendra Singh Chauhan
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The following message was posted to: PharmPK
Dear Harendra!
Harendra Singh Chauhan wrote:
> i would appreciate if any get me update me about the Group effect is
> mandatory or not to consider in statistical analysis [...]
Have you read my references? Any specific (!) questions?
> & How many days difference between confinement of two groups is
> relevant if there is any supportive article or guidance, [...]
FDA considers 'months apart' relevant.
Regards,
Helmut
-
Ing. Helmut SchAtz
BEBAC - Consultancy Services for
Bioequivalence and Bioavailability Studies
Neubaugasse 36/11
1070 Vienna, Austria
e-mail helmut.schuetz.at.bebac.at
web http://bebac.at/
contact http://bebac.at/Contact.htm
forum http://forum.bebac.at/
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The following message was posted to: PharmPK
Understanding the groups upfront is always good. If you did not, then
you
could reanalyze the data. For example, if on the original set you did
not
include time of day, month, sequence, sex you can add those groups to
see if
any additional effects can be associated with those factors. An
appropriate stat test may allow to discount a factor and collapse the
data.
Tests for equivalence or non-inferiority may be applicable.
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