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.
Level of Degree
Mathematics & Statistics
First Committee Member (Chair)
Second Committee Member
Third Committee Member
Dirichlet ProcessProcess; Nonparametric Bayes; Pharmacokinetics; Pharmacodynamics; Ordinary differential equations
Dong, Yan. "Nonparametric Bayes approach for a semi-mechanistic pharmacokinetic and pharmacodynamic model." (2015). http://digitalrepository.unm.edu/math_etds/67