[Population Modeling] Population modeling

[Roger Jelliffe]

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<http://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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