Verification on the effectiveness of minimum relative entropy method for pharmacokinetic analysis

最小相对熵法药代动力学分析有效性验证

• 批准号: 08672609
• 负责人: AMISAKI Takashi
• 金额: $ 0.64万
• 依托单位: Shimane University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1996
• 资助国家: 日本
• 起止时间: 1996 至 1997
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-08672609/
• 关键词: pharmacokinetics; parameter estimation; relative entropy; least squares

Primary aim of this research had been to examine the effectiveness of the minimum relative entropy method (MRE), which, in 1995, Amisaki and Eguchi proposed as the estimation method for phamacokinetic parameter analysis. However, as the research progressed, it revealed that MRE is mathematically identical to the iteratively reweighted least squares (IRLS) with a Poisson variance function. This result provides the theoretical basis for justifying the IRLS,that is, the method works by minimizing the relative entropy. A previous typical claim for the IRLS has been as follows : the method seems to perform well, but would be appropriate for data from a Poisson distribution, i.e., counted data. In addition to this result, this research theoretically and/or numerically compared the properties of many kinds of estimation methods, including weighted least squares, generalized least squares, extended least squares, and extended quasi-likelihood. As a result, it is shown that MRE is best suited for analyzing individual pharmacokinetic data where estimation of intra-individual variation of each parameter is of no concern.

本研究的主要目的是检验最小相对熵法 (MRE) 的有效性，该法是 Amisaki 和 Eguchi 于 1995 年提出的用于药物动力学参数分析的估计方法。然而，随着研究的进展，人们发现 MRE 在数学上与具有泊松方差函数的迭代重加权最小二乘法 (IRLS) 相同。这一结果为证明IRLS的合理性提供了理论基础，即该方法通过最小化相对熵来工作。 IRLS 先前的典型声明如下：该方法似乎表现良好，但适用于泊松分布的数据，即计数数据。除了这一结果之外，本研究还从理论上和/或数值上比较了多种估计方法的特性，包括加权最小二乘法、广义最小二乘法、扩展最小二乘法和扩展拟似然法。结果表明，MRE 最适合分析个体药代动力学数据，无需考虑每个参数的个体内变异估计。