Two new user-friendly methods to assess pharmacometric parameter identifiability on categorical and continuous scales.

ABSTRACT: Parameter identifiability methods assess whether the parameters of a model are uniquely determined by the observations. While the success of a model fit can provide some information on this, it can be valuable to determine identifiability before any fit has been attempted, or to separate identifiability from other issues. Two concepts that lean themselves well for identifiability analysis and have been underutilized are the sensitivity matrix (SM) and the Fisher information matrix (FIM). This paper presents two newly developed methods, one based on the SM and one based on the FIM. Both methods can assess local identifiability for a wide set of models, can be used with limited effort, and are freely available. The methods require the proposed model in the form of a set of differential equat

SUBMITTER: van Noort M 

PROVIDER: S-EPMC11247123 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature