A switching markov chain monte carlo method for statistical identifiability of nonlinear pharmacokinetics models

Seongho Kim, Lang Li

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

We study the convergence rate of MCMC on the statistically unidentifiable nonlinear model involving the Michaelis-Menten kinetic equation. We have shown that, under certain conditions, the convergence diagnosis of Raftery and Lewis (1992) is consistent with the convergence rate argued by Brooks and Roberts (1999). Therefore, different MCMC schemes developed in linear models are further extended and compared in nonlinear models. We demonstrate that the single component MCMC (SCM) scheme is faster than the group component MCMC (GCM) scheme on unidentifiable models, while GCM is faster than SCM when the model is statistically identifiable. A novel MCMC method is then developed using both SCM and GCM schemes, termed the Switching MCMC (SWM) method. The proposed SWM possesses an advantage in that it is able to estimate parameters regardless of the statistically identifiable situations. In addition, simulations and data analysis suggest a better performance of the proposed SWM algorithm than SCM and GCM.

Original languageEnglish
Pages (from-to)1199-1215
Number of pages17
JournalStatistica Sinica
Volume22
Issue number3
DOIs
StatePublished - Jul 2012

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Pharmacokinetics
Markov Chain Monte Carlo Methods
Identifiability
Markov Chain Monte Carlo
MCMC Methods
Model
Nonlinear Model
Markov chain Monte Carlo
Markov chain Monte Carlo methods
MCMC Algorithm
Simulation Analysis
Kinetic Equation
Convergence Rate
Linear Model
Data analysis
Rate of Convergence

Keywords

  • Convergence rate
  • Michaelis-Menten (MM) kinetics
  • Monte Carlo Markov chain (MCMC)
  • Pharmacokinetics (PK)
  • Statistical identifiability
  • Switching algorithm

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

A switching markov chain monte carlo method for statistical identifiability of nonlinear pharmacokinetics models. / Kim, Seongho; Li, Lang.

In: Statistica Sinica, Vol. 22, No. 3, 07.2012, p. 1199-1215.

Research output: Contribution to journalArticle

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