PHARMACOKINETIC & PHARMACODYNAMIC SYSTEMS ANALYSIS

在过去的一年里，我们实施并调查了一项 马尔可夫链蒙特卡罗方法，用于个体贝叶斯 药代动力学/药效学模型的估计。 的 实现了IMH算法 对于一般的a类pk/pd模型。 在该实现中， 先验密度可以指定为多元正态或 对数正态密度 这使我们能够调查案件时， 先验密度是多峰，这可能发生在例如当 少数受试者亚群的PK/PD参数与 主要人口密度。 IMH方法的实现是 目前用于个人贝叶斯估计的一部分， 圣裘德研究所的白消安临床方案 医院 我们也已经开始调查，使用的想法， 过程，再生MCMC方法。 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 第一次努力通过嵌入IMH产生再生马尔可夫链 非马尔可夫拒绝抽样方法中的抽样。 将合并的 该方法被称为拒绝IMH采样器（RIMH）。 虽然这 代表了最简单的非线性贝叶斯估计的例子，它 说明了方法的可行性，并将提供一个有用的 进一步探索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.