Practical identifiability of parametrised models: A review of benefits and limitations of various approaches

Abstract

This systematic review of practical identifiability (PI) explores the challenging issue of how parameter identification of models is affected by both experimental considerations and model structure. Structural identifiability (SI) analyses that yield binary assessment of parameter uniqueness have been historically dominant in the field. However, recent developments in the less explored PI domain have facilitated more nuanced estimates of identified model parameter trade-off and variance. As PI acknowledges variation in parameter estimates due to real-world limitations in data quality and quantity, it can both explore how parameters may trade-off, and guide more informative experimental design.In this review, PI analysis methodologies used across various fields of study are compared, and their role in aiding experimental design is discussed. The methods presented show that the choice of PI approach requires careful consideration based on the modelling context and desired research outcomes. Illustrative examples are included for common methodologies, and some ongoing research is briefly reviewed. Overall, the concept of PI brings value to model-based analyses across a broad range of disciplines.