Journal ArticleDOI
Optimal sampling times in population pharmacokinetic studies
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A simulation‐based approach to decision theoretic Bayesian optimal design is proposed for choosing sampling times for the anticancer agent paclitaxel, using criteria related to the total area under the curve, the time above a critical threshold and the sampling cost.Abstract:
We propose a simulation-based approach to decision theoretic Bayesian optimal design. The underlying probability model is a population pharmacokinetic model which allows for correlated responses (drug concentrations) and patient-to-patient heterogeneity. We consider the problem of choosing sampling times for the anticancer agent paclitaxel, using criteria related to the total area under the curve, the time above a critical threshold and the sampling cost.read more
Citations
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Journal ArticleDOI
A Review of Modern Computational Algorithms for Bayesian Optimal Design
TL;DR: A general overview on the concepts involved in Bayesian experimental design can be found in this article, where some of the more commonly used Bayesian utility functions and methods for their estimation, as well as a number of algorithms that are used to search over the design space to find the Bayesian optimal design.
Journal ArticleDOI
Bayesian experimental design for nonlinear mixed-effects models with application to HIV dynamics.
Cong Han,Kathryn Chaloner +1 more
TL;DR: In this paper, the authors investigated experimental design for Bayesian analysis of nonlinear mixed-effects models and established the existence of the posterior risk for parameter estimation for both design and inference.
Journal Article
Towards Bayesian experimental design for nonlinear models that require a large number of sampling times
TL;DR: A simulation-based approach that can be used to solve optimal design problems in which one is interested in finding a large number of (near) optimal design points for a small number of design variables is presented.
Journal ArticleDOI
Towards Bayesian experimental design for nonlinear models that require a large number of sampling times
TL;DR: In this article, a simulation-based approach that can be used to solve optimal design problems in which one is interested in finding a large number of (near) optimal design points for a small number of design variables is presented.
Fully Bayesian experimental design for pharmacokinetic studies
TL;DR: In this paper, the authors explore the use of Laplace approximations in the design setting to overcome the drawback that importance sampling will tend to break down if there is a reasonable number of experimental observations and/or the model parameter is high dimensional.
References
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Numerical recipes in C
TL;DR: The Diskette v 2.06, 3.5''[1.44M] for IBM PC, PS/2 and compatibles [DOS] Reference Record created on 2004-09-07, modified on 2016-08-08.
BookDOI
Markov Chain Monte Carlo in Practice
TL;DR: The Markov Chain Monte Carlo Implementation Results Summary and Discussion MEDICAL MONITORING Introduction Modelling Medical Monitoring Computing Posterior Distributions Forecasting Model Criticism Illustrative Application Discussion MCMC for NONLINEAR HIERARCHICAL MODELS.
Journal ArticleDOI
Sampling-Based Approaches to Calculating Marginal Densities
TL;DR: In this paper, three sampling-based approaches, namely stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm, are compared and contrasted in relation to various joint probability structures frequently encountered in applications.
Journal Article
Sampling-based approaches to calculating marginal densities
TL;DR: Stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm can be viewed as three alternative sampling- (or Monte Carlo-) based approaches to the calculation of numerical estimates of marginal probability distributions.
Book
Optimal Statistical Decisions
TL;DR: In this article, the authors present a survey of probability theory in the context of sample spaces and decision problems, including the following: 1.1 Experiments and Sample Spaces, and Probability 2.2.3 Random Variables, Random Vectors and Distributions Functions.