Random Measures, ANOVA Models and Quantifying Uncertainty in Randomized Controlled Trials
arxiv(2023)
摘要
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.
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