[Population Modeling] Population modeling

[Jacob Barhak]

Thanks Roger,

So indeed, how many modelers in this group use a similar estimation
approach to Roger?

Please do join this discussion regarding estimation techniques. If enough
of us do so, we can write a short summary like we did last year - only
this time focus on estimation techniques.

I hope enough of you will be interested to expose your estimation work for
population models this way.

                Jacob


On Thu, Oct 1, 2015 at 4:33 PM, Roger Jelliffe <jelliffe at usc.edu> wrote:

> Dear Jacob:
>
>          Thanks for your reply. Here is my two cents again.
>
> How many in the group use nonparametric methods for population analysis?
> These methods do not make any assumptions as to the presumed shape of the
> probability distribution of the model parameters – they are assumption
> free, and there are no constraints at all on what that distribution can be.
> The Pmetrics software by the USC Laboratory of Applied Pharmacokinetics and
> Bioinformatics makes this software available free over www.lapk.org.
> Actually, any system that can be described by ordinary differential
> equations can be analyzed with this software, not just
> pharmacokinetic/dynamic systems.  In addition, they permit maximally
> precise control of the system (dosage regimens of drugs, for example) using
> multiple model (MM) dosage design. At the web site, you can also access
> publications and technical reports dealing with the subject. For openers,
> one might look at these references:
>
> 1.    Bustad A, Terziivanov D, Leary R, Port R, Schumitzky A, and
> Jelliffe R: Parametric and Nonparametric Population Methods: Their
> Comparative Performance in Analysing a Clinical Data Set and Two Monte
> Carlo Simulation Studies. Clin. Pharmacokinet., 45: 365-383, 2006.
>
> 2.   Neely M, van Guilder M, Yamada W, Schumitzky A, and Jelliffe R:
> Accurate Detection of Outliers and Subpopulations with Pmetrics, a
> Nonparametric and Parametric Pharmacometric Modeling and Simulation Package
> for R. Therap. Drug Monit. 34: 467-476, 2012.
>
> 3.   Jelliffe R, Schumitzky A, Bayard D, Milman M, Van Guilder M, Wang X,
> Jiang F, Barbaut X, and Maire P: Model-Based, Goal-Oriented, Individualized
> Drug Therapy: Linkage of Population Modeling, New "Multiple Model" Dosage
> Design, Bayesian Feedback, and Individualized Target Goals. Clin.
> Pharmacokinet. 34: 57-77, 1998.
>
> Likelihoods are greater with NP models because the distributions are not
> constrained by any assumptions about its shape, such as normal, lognormal,
> bimodal, etc. The distributions simply are what they are. There is also a
> good Monte Carlo simulation routine that can do rigorous MC simulation even
> when the basic discrete structure of a nonparametric model with its
> multiple discrete support points is preserved.
>
> Best regards to all,
>
> Roger Jelliffe
>
>
>
>
>
>
>
> Roger W. Jelliffe, M.D., F.C.P., F.A.A.C.S.
>
> Professor of Medicine Emeritus,
>
> USC School of Medicine
>
> Founder and Director Emeritus
>
> Laboratory of Applied Pharmacokinetics and Bioinformatics
>
> Consultant in Infectious Diseases,
>
> Children’s Hospital of Los Angeles
>
> 4640 Hollywood Blvd, MS #30
>
> Los Angeles CA 90027
>
> Office - 323-361-5046
>
> Fax - 323-361-5045
>
> Cell - 626-484-5313
>
> jelliffe at usc.edu
>
> www.lapk.org
>
>
>
> Quantitative approaches
>
> to optimally precise individualized drug therapy
>
> are more caring,
>
> and useful,
>
> scientifically, medically and socially,
>
> than all the memorized words and facts
>
> of categorized and classified experience
>
> can ever be!
>
>
>
> _______________________________________________
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>
>
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