Automated covariate selection and Bayesian model averaging in population PK/PD models Authors
First Online: 08 November 2007 Received: 20 March 2007 Accepted: 01 October 2007 DOI:
Cite this article as: Lunn, D.J. J Pharmacokinet Pharmacodyn (2008) 35: 85. doi:10.1007/s10928-007-9077-x Abstract
We illustrate the use of ‘reversible jump’ MCMC to automate the process of covariate selection in population PK/PD analyses. The output from such an approach can be used not only to determine the ‘best’ covariate model for each parameter, but also to formally measure the spread of uncertainty across all possible models, and to average inferences across a range of ‘good’ models. We examine the substantive impact of such model averaging compared to conditioning inferences on the ‘best’ model alone, and conclude that clinically significant differences between the two approaches can arise. The illustrative data that we consider pertain to the drug vancomycin in 59 neonates and infants, and all analyses are conducted using the WinBUGS software with newly developed ‘Jump’ interface installed.
Keywords Bayesian model averaging Covariate/variable selection Markov chain Monte Carlo Reversible jump Vancomycin WinBUGS Download to read the full article text References
Akaike H (1974). A new look at statistical model identification.
IEET T Automat Contr
Vaida F and Blanchard S (2005). Conditional Akaike information for mixed-effects models.
Lavielle M and Mentré F (2007). Estimation of population pharmacokinetic parameters of saquinavir in HIV patients with the MONOLIX software.
J Pharmacokinet Pharmacodyn
Longford NT (2005). Editorial: model selection and efficiency—is ‘Which model ...?’ the right question?.
J R Statist Soc A
Hoeting JA, Madigan D, Raftery AE and Volinsky CT (1999). Bayesian model averaging: a tutorial.
Green PJ (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination.
Lunn DJ, Thomas A, Best N and Spiegelhalter D (2000). WinBUGS—a Bayesian modelling framework: concepts, structure, and extensibility.
Spiegelhalter D, Thomas A, Best N, Lunn D (2003) WinBUGS user manual, version 1.4. Medical Research Council Biostatistics Unit, Cambridge
Lunn DJ, Best N, Whittaker J (2005) Generic reversible jump MCMC using graphical models. Technical Report EPH-2005-01, Department of Epidemiology and Public Health, Imperial College London, UK
Lunn DJ, Whittaker JC and Best N (2006). A Bayesian toolkit for genetic association studies.
Grimsley C and Thomson AH (1999). Pharmacokinetics and dose requirements of vancomycin in neonates.
Arch Dis Child Fetal Neonatal Ed
Bernardo JM and Smith AFM (1994). Bayesian theory. John Wiley & Sons, New York
Wakefield J and Bennett J (1996). The Bayesian modeling of covariates for population pharmacokinetic models.
J Am Statist Ass
Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH and Teller E (1953). Equations of state calculations by fast computing machines.
J Chem Phys
Hastings WK (1970). Monte Carlo sampling-based methods using Markov chains and their applications.
Lunn DJ, Best N, Thomas A, Wakefield J and Spiegelhalter D (2002). Bayesian analysis of population PK/PD models: general concepts and software.
J Pharmacokinet Pharmacodyn
Lunn DJ, Wakefield J, Thomas A, Best N, Spiegelhalter D (1999) PKBugs user guide version 1.1. Dept. Epidemiology and Public Health, Imperial College School of Medicine, London
Lunn D (2006) WinBUGS ‘Jump’ Interface: Beta-Release User Manual. Dept. Epidemiology and Public Health, Imperial College School of Medicine, London
Brooks SP and Giudici P (1999). Convergence assessment for reversible jump MCMC simulations. In: Bernardo, JM, Berger, JO, Dawid, AP and Smith, AFM (eds) Bayesian statistics 6, pp 733–742. Oxford University Press, Oxford
Wakefield JC, Smith AFM, Racine-Poon A and Gelfand AE (1994). Bayesian analysis of linear and non-linear population models by using the Gibbs sampler.
Kang D, Verotta D (2007) Reversible jump Markov chain Monte Carlo for deconvolution. J Pharmacokinet Pharmacodyn doi:
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