在深模型中混合效应估计:变化方法优于一级近似方法
Alexander Janssen1, Frank C Bennis2, Marjon H Cnossen3
1Department of Clinical Pharmacology, Hospital Pharmacy, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands. a.janssen@amsterdamumc.nl.
Journal of pharmacokinetics and pharmacodynamics
|July 4, 2024
概括
这项研究将混合效应估计引入深模型 (DCM) 框架. 变异推理 (VI) 证明了针对个性化药物剂量的准确和稳定的预测,优于传统方法.
科学领域:
- 药学指标 (Pharmacometrics) 是一个指标.
- 计算生物学 计算生物学
- 统计建模 统计建模
背景情况:
- 深模型 (DCM) 是药理动力学/药理动力学 (PK/PD) 分析的强大工具.
- 估计DCM的混合效应对于个性化医学和优化治疗策略至关重要.
- 目前用于DCM中混合效应估计的方法在准确性和稳定性方面存在局限性.
研究的目的:
- 扩展深层隔间模型 (DCM) 框架用于混合效应估计.
- 为了比较第一阶 (FO,FOCE) 和变异推理 (VI) 算法的性能,用于DCM中的混合效应估计.
- 通过模拟和现实世界的数据来评估这些方法的准确性和稳定性.
主要方法:
- 在DCM框架内实施混合效应估计.
- 一级条件估计 (FOCE),一级 (FO) 和变量推理 (VI) 算法的比较.
- 使用模拟数据集和来自血友病A患者的真实数据进行验证.
主要成果:
- 使用路径衍生梯度估计器的变化推理 (VI) 在近似后部分布方面显示出高准确性.
- 在模拟中,FO和VI方法都产生了准确的种群参数和共变效应,而FOCE则显示不稳定性和不准确的估计.
- FO和VI方法在真实世界的血友病A数据上产生了类似的结果,其中一些FO模型显示出分歧;FOCE仍然不稳定.
结论:
- 使用深模型 (DCM) 估计混合效应是可行的和有效的.
- 变量推理 (VI) 为DCM中混合效应估计提供了一个稳定而准确的替代方案,在复杂模型中可能超过一级 (FO) 方法.
- 这些发现支持在药量计模型中使用VI进行个性化治疗优化.
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