Random Measures, ANOVA models, and Uncertainty Quantification of Randomized Controlled Trials

In this talk, Bastian will discuss a novel approach to global sensitivity analysis grounded in the variance-covariance structure of random variables derived from random measures. The proposed methodology facilitates the application of information-theoretic rules for uncertainty quantification, offering several advantages. Specifically, the approach provides valuable insights into the decomposition of variance within discrete subspaces, similar to the standard ANOVA analysis. To illustrate this point, the method is applied to datasets obtained from the analysis of randomized controlled trials on evaluating the efficacy of the COVID-19 vaccine, assessing clinical endpoints in a lung cancer study, and evaluating the efficacies of targeted therapeutics.