Global sensitivity analysis workflows and rankings: A practical comparison for researchers

Global sensitivity analysis (GSA) is a recommended step in the use of computer simulation models. GSA quantifies the relative importance of model inputs on outputs (Factor Ranking), identifies inputs that could be fixed, thus simplifying model calibration (Factor Fixing), and pinpoints areas for future data collection (Factor Prioritization). Given the wide variety of GSA methods, choosing between methods can be challenging. We provide a practitioner-focused guide for non-GSA experts that compares both widely and less commonly used GSA methods, discuss implementation and interpretation issues, and propose a workflow. We assess the degree of similarity in Factor Ranking based on a study of three simulators of differing complexity. A critical issue for all methods is specification of parameter ranges. Factor Rankings were generally quite similar based on Kendall’s W. Sobol’ first order and total sensitivity indices were easy to interpret and informative with regression trees providing additional insight into interactions. • A practitioner-focused framework for conducting Global Sensitivity Analysis (GSA) is presented clarifies implementation choices, interpretation, and limitations. • Included is a ten-step workflow for non-specialists. • Several GSA methods are applied to three real world simulators of varying complexity. • Results for factor ranking are relatively similar across the GSA methods with Sobol’ sensitivities and regression trees deemed attractive and complementary.