  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.
