In pharmaceutical research and development decision-making related to drug candidate selection, efficacy and safety is commonly supported through modelling and simulation (M\&S). Among others, physiologically-based pharmacokinetic models are used to describe drug absorption, distribution and metabolism in human. Global sensitivity analysis (GSA) is gaining interest in the pharmacological M\&S community as an important element for quality assessment of model-based inference. Physiological models often present inter-correlated parameters. The inclusion of correlated factors in GSA and the sensitivity indices interpretation has proven an issue for these models. Here we devise and evaluate a latent variable approach for dealing with correlated factors in GSA. This approach describes the correlation between two model inputs through the causal relationship of three independent factors: the latent variable and the unique variances of the two correlated parameters. Then, GSA is performed with the classical variance-based method. We applied the latent variable approach to a set of algebraic models and a case from physiologically-based pharmacokinetics. Then, we compared our approach to Sobol's GSA assuming no correlations, Sobol's GSA with groups and the Kucherenko approach. The relative ease of implementation and interpretation makes this a simple approach for carrying out GSA for models with correlated input factors.
How does academic performance affect self-efficacy? Interpretable modelling through latent academic achievement
Human Technology Institute, University of Technology Sydney（人类技术研究所，悉尼科技大学）；School of Mathematical and Physical Sciences, University of Technology Sydney（数理科学学院，悉尼科技大学）
A spatio-temporal block aggregation model for latent log Gaussian outcomes: application on modelling wastewater virus concentration in Wales
Imperial College London（帝国理工学院）；Lancaster University（兰卡斯特大学）；Northumbria University（诺森比亚大学）；UK Health Security Agency（英国卫生安全局）；Public Health Wales（威尔士公共卫生局）；Bangor University（班戈大学）
