A Bayesian approach to parameter estimation in HIV dynamical models

In the context of a mathematical model describing HIV infection, we discuss a Bayesian modelling approach to a non‐linear random effects estimation problem. The model and the data exhibit a number of features that make the use of an ordinary non‐linear mixed effects model intractable: (i) the data a...

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Bibliographic Details
Published in:Statistics in medicine 2002-08, Vol.21 (15), p.2199-2214
Main Authors: Putter, H., Heisterkamp, S. H., Lange, J. M. A., de Wolf, F.
Format: Article
Language:English
Subjects:
Bayes Theorem
Biological and medical sciences
CD4-Positive T-Lymphocytes - immunology
Computer Simulation
HIV
HIV Infections - immunology
HIV Infections - virology
HIV-1 - growth & development
Humans
Markov Chains
mathematical model
mathematical model, MCMC
MCMC
Medical sciences
Models, Biological
Monte Carlo Method
non-linear mixed effects
RNA, Viral - blood
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Summary:In the context of a mathematical model describing HIV infection, we discuss a Bayesian modelling approach to a non‐linear random effects estimation problem. The model and the data exhibit a number of features that make the use of an ordinary non‐linear mixed effects model intractable: (i) the data are from two compartments fitted simultaneously against the implicit numerical solution of a system of ordinary differential equations; (ii) data from one compartment are subject to censoring; (iii) random effects for one variable are assumed to be from a beta distribution. We show how the Bayesian framework can be exploited by incorporating prior knowledge on some of the parameters, and by combining the posterior distributions of the parameters to obtain estimates of quantities of interest that follow from the postulated model. Copyright © 2002 John Wiley & Sons, Ltd.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.1211