基于最大后验估计的群体药代动力学/药效学混合模型
Population Pharmacokinetic/Pharmacodyanamic Mixture Models via Maximum a Posteriori Estimation.
作者信息
Wang Xiaoning, Schumitzky Alan, D'Argenio David Z
机构信息
Clinical Discovery, Strategic Modeling & Simulation Group, Bristol-Myers Squibb Co., Princeton, NJ 08543, USA.
出版信息
Comput Stat Data Anal. 2009 Oct 1;53(12):3907-3915. doi: 10.1016/j.csda.2009.04.017.
Abstract
Pharmacokinetic/pharmacodynamic phenotypes are identified using nonlinear random effects models with finite mixture structures. A maximum a posteriori probability estimation approach is presented using an EM algorithm with importance sampling. Parameters for the conjugate prior densities can be based on prior studies or set to represent vague knowledge about the model parameters. A detailed simulation study illustrates the feasibility of the approach and evaluates its performance, including selecting the number of mixture components and proper subject classification.
摘要
使用具有有限混合结构的非线性随机效应模型来识别药代动力学/药效学表型。提出了一种使用带有重要性抽样的期望最大化（EM）算法的最大后验概率估计方法。共轭先验密度的参数可以基于先前的研究设定，或者设定为表示对模型参数的模糊认知。一项详细的模拟研究说明了该方法的可行性，并评估了其性能，包括选择混合成分的数量和进行适当的受试者分类。
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