What do we mean by identifiability in mixed effects models?
What do we mean by identifiability in mixed effects models?
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DOI：
10.1007/s10928-015-9459-4
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发表时间：
2016-02-01
影响因子：
2.5
通讯作者：
Aarons, Leon
中科院分区：
文献类型：
--
作者：
Lavielle, Marc;Aarons, Leon
我们讨论的非线性混合效应模型的背景下，模型的可识别性的问题。虽然在固定效应模型领域已经有了广泛的研究，但对随机效应模型的关注却少得多。在这种情况下，我们区分理论的可识别性，其中不同的参数值导致不相同的概率分布，结构的可识别性，其中涉及的代数性质的结构模型，和实际的可识别性，从而该模型可能是理论上可识别的，但实验的设计可能会使参数估计困难和不精确。我们探索了许多药代动力学模型，已知这些模型在个体水平上不可识别，但如果概率模型的许多特定假设成立，则可以在群体水平上识别。本质上，如果概率模型不同，即使结构模型是不可识别的，那么它们将导致不同的可能性。研究结果通过模拟得到支持。
We discuss the question of model identifiability within the context of nonlinear mixed effects models. Although there has been extensive research in the area of fixed effects models, much less attention has been paid to random effects models. In this context we distinguish between theoretical identifiability, in which different parameter values lead to non -identical probability distributions, structural identifiability which concerns the algebraic properties of the structural model, and practical identifiability, whereby the model may be theoretically identifiable but the design of the experiment may make parameter estimation difficult and imprecise. We explore a number of pharmacokinetic models which are known to be non -identifiable at an individual level but can become identifiable at the population level if a number of specific assumptions on the probabilistic model hold. Essentially if the probabilistic models are different, even though the structural models are non -identifiable, then they will lead to different likelihoods. The findings are supported through simulations.