Fully Bayesian Experimental Design for Pharmacokinetic Studies
Fully Bayesian Experimental Design for Pharmacokinetic Studies
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DOI：
10.3390/e17031063
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发表时间：
2015-03-01
期刊：
影响因子：
2.7
通讯作者：
Pettitt, Anthony N.
中科院分区：
文献类型：
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作者：
Ryan, Elizabeth G.;Drovandi, Christopher C.;Pettitt, Anthony N.
贝叶斯试验设计中的效用函数通常基于后验分布。当通过模拟找到后验分布时，必须对从先验预测分布中提取的每个未来数据集进行采样。通常需要数千个后验分布。在贝叶斯实验设计文献中，一种流行的技术是重要性抽样，它使用先验作为重要性分布，可以快速地从后验中获得样本。然而，如果存在合理数量的实验观察，则来自先验的重要性抽样将倾向于失效。在本文中，我们探讨了使用拉普拉斯近似的设计设置，以克服这一缺点。此外，我们考虑使用拉普拉斯近似来形成重要性分布，以获得比先验更有效的重要性分布。该方法的动机是药代动力学研究，研究体外膜肺氧合对抗生素在绵羊体内药代动力学的影响。设计问题是找到10个接近最佳的血浆采样时间，这些时间可以精确估计药代动力学模型参数/目标测量值。在这些研究中，我们考虑了几个不同的效用函数，其中涉及参数函数的后验分布。
Utility functions in Bayesian experimental design are usually based on the posterior distribution. When the posterior is found by simulation, it must be sampled from for each future dataset drawn from the prior predictive distribution. Many thousands of posterior distributions are often required. A popular technique in the Bayesian experimental design literature, which rapidly obtains samples from the posterior, is importance sampling, using the prior as the importance distribution. However, importance sampling from the prior will tend to break down if there is a reasonable number of experimental observations. In this paper, we explore the use of Laplace approximations in the design setting to overcome this drawback. Furthermore, we consider using the Laplace approximation to form the importance distribution to obtain a more efficient importance distribution than the prior. The methodology is motivated by a pharmacokinetic study, which investigates the effect of extracorporeal membrane oxygenation on the pharmacokinetics of antibiotics in sheep. The design problem is to find 10 near optimal plasma sampling times that produce precise estimates of pharmacokinetic model parameters/measures of interest. We consider several different utility functions of interest in these studies, which involve the posterior distribution of parameter functions.