Combining MCMC with 'sequential' PKPD modelling
Combining MCMC with 'sequential' PKPD modelling
复制标题
DOI：
10.1007/s10928-008-9109-1
复制
发表时间：
2009-02-01
影响因子：
2.5
通讯作者：
Neuenschwander, Beat
中科院分区：
文献类型：
--
作者：
Lunn, David;Best, Nicky;Neuenschwander, Beat
我们引入了一种防止贝叶斯 PKPD 链路模型中不需要的反馈的方法。我们使用一个针对单个个体的简单示例来说明该方法，然后演示它可以轻松地应用于更一般的设置。特别是，我们研究了由Zhang等人研究的三个“连续”群体PKPD模型。 (J Pharmacokinet Pharmacodyn 30:387-404, 2003; J Pharmacokinet Pharmacodyn 30:405-416, 2003)，并提供这些模型的图形表示以阐明它们的结构。我们方法的一个重要特点是它允许 PK 参数的不确定性传播到 PD 参数的推论。这与标准两阶段方法相反，标准两阶段方法需要对群体或个体特定的 PK 参数进行“插入”点估计。
We introduce a method for preventing unwanted feedback in Bayesian PKPD link models. We illustrate the approach using a simple example on a single individual, and subsequently demonstrate the ease with which it can be applied to more general settings. In particular, we look at the three 'sequential' population PKPD models examined by Zhang et al. (J Pharmacokinet Pharmacodyn 30:387-404, 2003; J Pharmacokinet Pharmacodyn 30:405-416, 2003), and provide graphical representations of these models to elucidate their structure. An important feature of our approach is that it allows uncertainty regarding the PK parameters to propagate through to inferences on the PD parameters. This is in contrast to standard two-stage approaches whereby 'plug-in' point estimates for either the population or the individual-specific PK parameters are required.