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Re: [NMusers] unbalanced data set

From: Leonid Gibiansky <>
Date: Wed, 6 Jan 2016 11:32:15 -0500

I recently found out that FDA approved digestible sensor that can be
given with the tablet (any tablet) and inform the patient (and the
company if needed) whether and when the tablet was taken

If used in the trials, it would end the guessing game about dose times,
compliance, etc., providing the exact times of doses for the analysis.

I am wondering whether anybody has an experience with this type of data?
It would be interesting to see the difference between diary-based
analysis and sensor-based analysis.


Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
e-mail: LGibiansky at
tel: (301) 767 5566

On 1/6/2016 9:55 AM, Michael Fossler wrote:
> 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
> <>
> Office: 610-354-8840, ext. 249
> Cell: 610-329-6636
> *From:*
> [] *On Behalf Of *Denney, William S.
> *Sent:* Wednesday, January 06, 2016 8:33 AM
> *To:* <>
> *Cc:* Zheng Liu;
> *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" <
> <>> 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
> <>
> *From:*
> <>
> [] *On Behalf Of *Zheng Liu
> *Sent:* 06 January 2016 02:03
> *To:* <>
> *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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Received on Wed Jan 06 2016 - 11:32:15 EST

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