标题： 非参数方法在非线性混合效应模型实际可辨识性中的应用 显示英文标题

标题： A nonparametric approach to practical identifiability of nonlinear mixed effects models

摘要： 数学建模是一种广泛用于理解和解释临床试验数据的方法。 这种建模通常涉及将机制性数学模型拟合到个别试验参与者的数据上。 尽管基于个体的拟合方法已被广泛采用，但采取分层参数估计方法变得越来越常见，其中建模者描述总体参数分布，而不是独立考虑每个个体。 这种分层参数估计在药动学建模中是标准做法。 然而，许多现有的参数可识别性技术并不能直接从基于个体的拟合转移到分层设置中。 在这里，我们提出了一种非参数方法，在分层参数估计框架内研究实际可识别性。 我们专注于常用的非线性混合效应框架，并研究药动学和病毒动力学文献中两个广泛研究的例子，以说明我们方法的潜在实用性。 显示英文摘要

摘要： Mathematical modelling is a widely used approach to understand and interpret clinical trial data. This modelling typically involves fitting mechanistic mathematical models to data from individual trial participants. Despite the widespread adoption of this individual-based fitting, it is becoming increasingly common to take a hierarchical approach to parameter estimation, where modellers characterize the population parameter distributions, rather than considering each individual independently. This hierarchical parameter estimation is standard in pharmacometric modelling. However, many of the existing techniques for parameter identifiability do not immediately translate from the individual-based fitting to the hierarchical setting. Here, we propose a nonparametric approach to study practical identifiability within a hierarchical parameter estimation framework. We focus on the commonly used nonlinear mixed effects framework and investigate two well-studied examples from the pharmacometrics and viral dynamics literature to illustrate the potential utility of our approach.