Navigating the landscape of parameter identifiability methods: A workflow recommendation for model development.

ABSTRACT: In pharmacometric modeling, it is often important to know whether the data is sufficiently rich to identify the parameters of a proposed model. While it may be possible to assess this based on the results of a model fit, it is often difficult to disentangle identifiability issues from other model fitting and numerical problems. Furthermore, it can be of value to ascertain identifiability beforehand. This paper compares four methods for parameter identifiability, namely Differential Algebra for Identifiability of SYstems (DAISY), the sensitivity matrix method (SMM), Aliasing, and the Fisher information matrix method (FIMM). We discuss the characteristics of the methods and apply them to a set of applications, consisting of frequently used PK model structures, with suitable dosing regimens a

SUBMITTER: van Noort M 

PROVIDER: S-EPMC11247124 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature