Search Results - "Schumitzky, A"

An adaptive grid non-parametric approach to pharmacokinetic and dynamic (PK/PD) population models by Leary, R., Jelliffe, R., Schumitzky, A., Van Guilder, M.

“…Our NPEM (Non-Parametric Expectation Maximization) software for non-parametric PK/PD (pharmacokinetic/pharmacodynamic) population modeling employs the…”
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Individualizing drug dosage regimens: roles of population pharmacokinetic and dynamic models, Bayesian fitting, and adaptive control by Jelliffe, R W, Schumitzky, A, Van Guilder, M, Liu, M, Hu, L, Maire, P, Gomis, P, Barbaut, X, Tahani, B

“…The role of population pharmacokinetic modeling is to store experience with drug behavior. The behavior of the model is then correlated with the clinical…”
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Accurately Achieving Target Busulfan Exposure in Children and Adolescents With Very Limited Sampling and the BestDose Software by Neely, Michael, Philippe, Michael, Rushing, Teresa, Fu, Xiaowei, van Guilder, Michael, Bayard, David, Schumitzky, Alan, Bleyzac, Nathalie, Goutelle, Sylvain

“…Busulfan dose adjustment is routinely guided by plasma concentration monitoring using 4-9 blood samples per dose adjustment, but a pharmacometric Bayesian…”
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Population pharmacokinetic and dynamic models: parametric (P) and nonparametric (NP) approaches by Jelliffe, R., Schumitzky, A., Van Guilder, M., Wang, X., Leary, R.

“…With parametric (P) models, the probability distributions of each PK/PD (pharmacokinetic/pharmacodynamic) model parameter are described as means and…”
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Parametric and nonparametric population methods : Their comparative performance in analysing a clinical dataset and two monte carlo simulation studies by BUSTAD, Aida, TERZIIVANOV, Dimiter, LEARY, Robert, PORT, Ruediger, SCHUMITZKY, Alan, JELLIFFE, Roger

“…This study examined parametric and nonparametric population modelling methods in three different analyses. The first analysis was of a real, although small,…”
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Multiple model (MM) dosage design: achieving target goals with maximal precision by Jelliffe, R., Bayard, D., Schumitzky, A., Milman, M., Jiang, F., Leonov, S., Gandhi, V., Gandhi, A., Botnen, A.

“…Dosage regimens based on parametric population models use single values to describe each parameter distribution. When a target goal is selected, the regimen to…”
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Design of dosage regimens: a multiple model stochastic control approach by Bayard, D S, Milman, M H, Schumitzky, A

“…This paper presents a general stochastic control framework for determining drug dosage regimens where the sample times, dosing times, desired goals, etc.,…”
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Pharmaco-informatics: more precise drug therapy from "multiple model" (MM) stochastic adaptive control regimens: evaluation with simulated vancomycin therapy by Jelliffe, R W, Bayard, D, Schumitzky, A, Milman, M, Van Guilder, M

“…MM stochastic control of dosage regimens permits essentially full use of information, either in a population pharmacokinetic model or a Bayesian updated MM…”
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Nonpharmacokinetic Clinical Factors Affecting Aminoglycoside Therapeutic Precision: A Simulation Study by Jelliffe, R. W., Schumitzky, A., Guilder, M.

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Modeling, adaptive control, and optimal drug therapy by Jelliffe, R W, Schumitzky, A

“…Drug therapy, its clearly development, and the advent of pharmacokinetic models are described, from the original work of Teorell, through that of Augsberger…”
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Optimal impulsive control of compartment models, I: Qualitative aspects by Pierce, J. G., Schumitzky, A.

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Model-based, goal-oriented, individualised drug therapy : Linkage of population modelling, new 'multiple model' dosage design, Bayesian feedback and individualised target goals by JELLIFFE, R. W, SCHUMITZKY, A, BAYARD, D, MILNAN, M, VAN GUILDER, M, WANG, X, JIANG, F, BARBAUT, X, MAIRE, P

“…This article examines the use of population pharmacokinetic models to store experiences about drugs in patients and to apply that experience to the care of new…”
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Kullback-Leibler Markov chain Monte Carlo--a new algorithm for finite mixture analysis and its application to gene expression data by Tatarinova, Tatiana, Bouck, John, Schumitzky, Alan

“…In this paper, we study Bayesian analysis of nonlinear hierarchical mixture models with a finite but unknown number of components. Our approach is based on…”
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SAAM II: Simulation, analysis, and modeling software for tracer and pharmacokinetic studies by Barrett, P.Hugh R., Bell, Bradley M., Cobelli, Claudio, Golde, Hellmut, Schumitzky, Alan, Vicini, Paolo, Foster, David M.

“…Kinetic analysis and integrated systems modeling have contributed substantially to our understanding of the physiology and pathophysiology of metabolic systems…”
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Achieving target goals most precisely using nonparametric compartmental models and "multiple model" design of dosage regimens by Jelliffe, R, Bayard, D, Milman, M, Van Guilder, M, Schumitzky, A

“…Multiple model (MM) design and stochastic control of dosage regimens permit essentially full use of all the information contained in either a Bayesian prior…”
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Accurate vs. approximate likelihood methods in population analysis of sparse data sets by Leary, R. H., Jelliffe, R., Schumitzky, A., Port, R. E.

“…Objective Compare the bias, statistical consistency, and statistical efficiency of conventional NONMEM and similar parametric population pharmacokinetic…”
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Deconvolving blood-alcohol concentration and alcohol beverage consumption from sensor measurements of transdermal alcohol by Dumett, M.A., Piterbarg, Y., Rosen, I.G., Schumitzky, A.

“…We utilize mathematical, statistical and deconvolution techniques to estimate blood alcohol concentration and alcohol ingestion rate from vapor alcohol…”
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Population pharmacokinetics/pharmacodynamics modeling: parametric and nonparametric methods by Jelliffe, R, Schumitzky, A, Van Guilder, M

“…As clinicians acquire experience with the clinical and pharmacokinetic behavior of a drug, it is usually optimal to record this experience in the form of a…”
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Optimal impulsive control of compartment models, II: Algorithm by Pierce, J. G., Schumitzky, A.

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A relative weighting method for estimating parameters and variances in multiple data sets by Bell, Bradley M., Burke, James V., Schumitzky, Alan

“…We are given multiple data sets and a nonlinear model function for each data value. Each data value is the sum of its error and its model function evaluated at…”
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