This short paper introduces 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 and assessing clinical endpoints in a lung cancer study.

翻译：本文提出了一种基于随机测度衍生随机变量方差-协方差结构的全局灵敏度分析新方法。该方法通过信息论规则实现不确定性量化，具有多重优势：不仅为离散子空间中的方差分解提供深刻见解（类似于标准方差分析），更通过COVID-19疫苗效价评估及肺癌临床试验临床终点评价两个随机对照试验数据集的应用实例，验证了该方法的有效性。