Author

Yan Dong

Publication Date

6-23-2015

Abstract

Both frequentist and Bayesian approaches have been used to characterize population pharmacokinetics and pharmacodynamics(PK/PD) models. These methods focus on estimating the population parameters and assessing the association between the characteristics of PK/PD and the subject covariates. In this work, we propose a Dirichlet process mixture model to classify the patients based on their individualized pharmacokinetic and pharmacodynamic profiles. Then we can predict the new patients dose-response curves given their concentration-time profiles. Additionally, we implement a modern Markov Chain Monte Carlo algorithm for sampling inference of parameters. The detailed sampling procedures as well as the results are discussed in a simulation data and a real data example. We also evaluate an approximate solution of a system of nonlinear differential equations from Euler's method and compare the results with a general numerical solver, ode from R package, deSolve.

Degree Name

Statistics

Level of Degree

Doctoral

Department Name

Mathematics & Statistics

First Advisor

Christensen, Ronald

First Committee Member (Chair)

Guindani, michele

Second Committee Member

Huerta, Gabriel

Third Committee Member

Erhardt, Erik

Language

English

Keywords

Dirichlet ProcessProcess; Nonparametric Bayes; Pharmacokinetics; Pharmacodynamics; Ordinary differential equations

Document Type

Dissertation

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