PHARMACOKINETIC & PHARMACODYNAMIC SYSTEMS ANALYSIS

在过去的拨款年度，我们实施并调查了一项 用于个体贝叶斯的马尔可夫链蒙特卡罗方法 药代动力学/药效学模型的估计。这个 实现了独立Metropolis-Hastings(IMH)算法 对于一般的A类PK/PD模型。在此实现中， 先验密度可以指定为多变量正态或 对数正态密度。这使我们能够在以下情况下调查案件 先前的密度是多模式的，这可能发生在例如当 少数受试者的PK/PD参数不同于 主要的人口密度。我们对IMH方法的实现是 当前用于单个贝叶斯估计，作为 圣裘德研究中心药物白消安的临床方案 医院。我们也已经开始调查，利用更新的想法 进程，一种可再生的MCMC方法。其动机是提供 作为基于样本的估计方案的一部分的误差分析。我们的 第一个努力通过嵌入一个IMH产生一个再生的马尔可夫链 非马尔可夫拒绝抽样方法内的抽样。合并后的 方法称为拒绝IMH采样器(RIMH)。虽然这件事 表示最简单的非线性贝叶斯估计示例，它 确实说明了该方法的可行性，并将提供一个有用的 进一步探讨RIMH性能的初始示例 算法。

During this past grant year we have implemented and investigated a Markov Chain Monte Carlo method, for use in individual Bayesian estimation of pharmacokinetic/pharmacodynamic models. The Independence Metropolis-Hastings (IMH) algorithm has been implemented for the general class a pk/pd models. In this implementation the prior density can be specified as a mixture of multivariate Normal or Lognormal densities. This has allowed us to investigate the case when the prior density is multimodal, which may occur, for example, when a minor subpopulation of subjects has pk/pd parameters distinct from the major population density. Our implementation of the IMH method is currently being used for individual Bayesian estimation as part of a clinical protocol of the drug Busulfan at the Saint Jude Research Hospital. We have also begun to investigate, using ideas from renewal processes, a regenerative MCMC method. The motivation is to provide an error analysis as part of a sample-based estimation scheme. Our first effort produces a regenerative Markov Chain by imbedding a IMH sampling within a non-Markov rejection sampling method. The combined method is referred to as a reject IMH sampler (RIMH). While this represents the simplest of nonlinear Bayesian estimation examples, it does illustrate the methods feasibility and will provide a useful initial example to further explore the performance of the RIMH algorithm.