RE: [NMusers] unbalanced data set

From: Joachim Grevel <jgrevel_at_btconnect.com>
Date: Wed, 6 Jan 2016 08:26:35 +0000

Dear Zheng,

This is indeed a fundamental and recurring problem in drug development. You
have rich data from Phase 1 studies (single ascending dose, multiple
ascending dose, others e.g. QTc) and sparse data from Phase 3 studies.
Should you mix them all in one large meta-analysis and derive the definitive
popPK model for that drug/project?

After years of experience, I tend to not mix Phase 1 with Phase 3 data.
Phase 1 can be used to establish the first popPK model which may contain
special features such as nonlinearities/saturation effects as a consequence
of the wide range of doses studied. This can be the starting point for the
building of a fit-for purpose model using Phase 3 data only. I have come to
believe that the specific patient population(s) of Phase 3 require their own
popPK model that predicts exposure without bias. This is then used in the
exposure-response (E-R) modelling that is important for market approval.
Only a dedicated Phase 3 popPK model, that does not carry unnecessary
legacies of Phase 1 development, is fit for E-R modelling and can give the
important answers about the dose rate(s) to be put in the drug label.

 

I would be interested to hear some other opinions.

 

Good luck,

Joachim

 

Joachim Grevel, PhD

Scientific Director

BAST Inc Limited

Science & Enterprise Park

Loughborough University

Loughborough, LE11 3AQ

United Kingdom

 

Tel: +44 (0)1509 222908

www.bastinc.eu <http://www.bastinc.eu/>

 

 

 

From: owner-nmusers_at_globomaxnm.com [mailto:owner-nmusers_at_globomaxnm.com] On
Behalf Of Zheng Liu
Sent: 06 January 2016 02:03
To: nmusers_at_globomaxnm.com
Subject: [NMusers] unbalanced data set

 

Dear all,

 

I recently have a data set for pk parameters fitting. The issue is some
patients have far more measurement points than others (i.e. a few patients
have ~15 points, other patients have only 1 or 2). I speculate in the fitted
parameters, those patients with many points would contribute much more than
those with less points. Then the population "average" values of fitted pk
parameters are not anymore average from all the patients, but more biased to
those patients with many points. This is not what I expect.

 

Of course I could take away some points from the patients with many points,
in order to be comparable to less-points patients. Then I will be forced to
lose some information from the data set. I just wonder are there anyone who
have better proposal to solve this problem? I appreciate your help very
much!

 

Best regards,

 

Zheng


Received on Wed Jan 06 2016 - 03:26:35 EST

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