RE: unbalanced data set
At the risk of being tiresome about this topic, absent specific differences
between Phase 1 and Phase 2/3 data , e.g., renal function due to age or disease
states, etc., I'd argue that most of the differences seen between Phase 1 and
Phase 2/3 data are due to adherence. In a sense, then, much of the differences
in PK between these two groups is artificial, and due to the fact that patients
do not reliably take their medication as prescribed, as opposed to Phase 1
volunteers, where adherence is near 100%. Bernard Vrijens has published a lot
on this topic as it relates to PPK analyses. We, as a discipline, need to start
pushing hard for adherence measures in clinical trials.
As an n=1 case study , a few years ago, I was involved with an analysis of a
large Phase 2 study which consisted of an in-house phase, followed by discharge
to home and an out-patient phase. The patients were significantly older and
sicker than Phase 1 volunteers, so one might expect some PK differences. When
we analyzed the data from the in-house portion of the study, we got results
nearly identical to Phase 1. However, when we added in the out-patient phase,
IIV on many of the parameters increased dramatically, and the residual error
became extremely large. Clearly, patients were not taking their medication as
prescribed ( and as they wrote in their patient diaries). We ended up not using
the out-patient portion of the data, which represents a huge waste of resources.
This irritates people when I say this, but we as a discipline are so enamored
of finding that magical covariate(s) which will explain variability, but we
neglect the most important one of all: Did they take the medicine when they say
they did? No biological covariate can have as big of an effect as adherence.
Accounting for adherence routinely results in up to a 50% decrease in residual
variability - few standard covariates have this effect.
Fossler M.J. Commentary: Patient Adherence: Clinical Pharmacology's
Embarrassing Relative. Journal of Clinical Pharmacology (2015) 55(4): 365-367.
Mike
Michael J. Fossler, Pharm. D., Ph. D., F.C.P.
VP, Quantitative Sciences
Trevena, Inc
[email protected]<mailto:[email protected]>
Office: 610-354-8840, ext. 249
Cell: 610-329-6636
Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of Denney, William S.
Sent: Wednesday, January 06, 2016 8:33 AM
To: <[email protected]>
Cc: Zheng Liu; [email protected]
Subject: Re: [NMusers] unbalanced data set
Hi Zheng,
I'll take an intermediate view between Joachim and Nick.
The rich data from Phase 1 provides the ability to define the structural model
and a few of the important covariates. The control of Phase 1 gives precision
that cannot be achieved in Phase 2 or 3 studies. But, there are usually
important differences between Phase 1 and later phase populations that makes
the later phase separately important.
With later phase trials, the range of covariates is expanded [1]. On top of
the expanded covariate range, sometimes late-phase patient populations are
categorically different than early phase [2].
In practice, this means that I fit a single model to all data. The model will
allow for the dense data from Phase 1 with more inter-individual variability
(IIV) terms (fix the IIV to 0 for sparse data) and the expanded covariate range
with a richer set of fixed effects as the model is expanded for later phase.
Finally, due to typical differences in data quality, I will often include a
different residual error structure for sparse data. This approach allows the
complexity of the Phase 1 structural model to carry into the richness of the
late phase covariate model.
[1] A specific example is that typically renal function is allowed to be lower
especially when Phase 1 is in healthy subjects.
[2] My true belief is that there may be unobserved covariates causing what
appears to be a categorical difference. The functional impact of that belief
is semantic only. In practice, the model would include a categorical parameter.
Thanks,
Bill
On Jan 6, 2016, at 4:09, "Joachim Grevel"
<[email protected]<mailto:[email protected]>> wrote:
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_&d=CwMFAg&c=UE1eNsedaKncO0Yl_u8bfw&r=4WqjVFXRfAkMXd6y3wiAtxtNlICJwFMiogoD6jkpUkg&m=wrsdorQ-9eTdtCeqy58cKOuX_NzLV7qeQgXnv6Rs89U&s=3ER4IQI_zP2M4rkqPEVwQseSkXSfoC6ux5FHzM7qeSs&e=">https://urldefense.proofpoint.com/v2/url?u=http-3A__www.bastinc.eu_&d=CwMFAg&c=UE1eNsedaKncO0Yl_u8bfw&r=4WqjVFXRfAkMXd6y3wiAtxtNlICJwFMiogoD6jkpUkg&m=wrsdorQ-9eTdtCeqy58cKOuX_NzLV7qeQgXnv6Rs89U&s=3ER4IQI_zP2M4rkqPEVwQseSkXSfoC6ux5FHzM7qeSs&e=
From: [email protected]<mailto:[email protected]>
[mailto:[email protected]] On Behalf Of Zheng Liu
Sent: 06 January 2016 02:03
To: [email protected]<mailto:[email protected]>
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
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