{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-latent-variable-approach-to-account-for","title":"A latent variable approach to account for correlated inputs in global sensitivity analysis with cases from pharmacological systems modelling","arxiv_id":"2012.02500","date":"2020-12-04","proceeding":null,"authors":["Nicola Melillo","Adam S. Darwich"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2012.02500v1","url_pdf":"https://arxiv.org/pdf/2012.02500v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"a-latent-variable-approach-to-account-for","repo_url":"https://github.com/NicolaMelillo/latent_variable_GSA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}