Department of Biomedical Sciences and Public Health, School of Medicine and Surgery, Marche Polytechnic University, Ancona, Italy.
Department of Communication and Economics, University of Modena and Reggio Emilia, Reggio Emilia, Italy.
PLoS One. 2021 Jun 17;16(6):e0253057. doi: 10.1371/journal.pone.0253057. eCollection 2021.
Real World Evidence (RWE) and its uses are playing a growing role in medical research and inference. Prominently, the 21st Century Cures Act-approved in 2016 by the US Congress-permits the introduction of RWE for the purpose of risk-benefit assessments of medical interventions. However, appraising the quality of RWE and determining its inferential strength are, more often than not, thorny problems, because evidence production methodologies may suffer from multiple imperfections. The problem arises to aggregate multiple appraised imperfections and perform inference with RWE. In this article, we thus develop an evidence appraisal aggregation algorithm called EA3. Our algorithm employs the softmax function-a generalisation of the logistic function to multiple dimensions-which is popular in several fields: statistics, mathematical physics and artificial intelligence. We prove that EA3 has a number of desirable properties for appraising RWE and we show how the aggregated evidence appraisals computed by EA3 can support causal inferences based on RWE within a Bayesian decision making framework. We also discuss features and limitations of our approach and how to overcome some shortcomings. We conclude with a look ahead at the use of RWE.
真实世界证据（RWE）及其用途在医学研究和推论中发挥着越来越重要的作用。值得注意的是，2016 年美国国会批准的《21 世纪治愈法案》允许引入 RWE，以评估医疗干预措施的风险效益。然而，评估 RWE 的质量并确定其推理强度通常是棘手的问题，因为证据产生方法可能存在多种缺陷。当需要综合多个已评估的缺陷并使用 RWE 进行推断时，问题就出现了。在本文中，我们因此开发了一种称为 EA3 的证据评估综合算法。我们的算法采用了 softmax 函数——逻辑函数在多维空间上的推广，该函数在统计学、数学物理和人工智能等多个领域都很流行。我们证明了 EA3 具有评估 RWE 的一些理想特性，并展示了如何在贝叶斯决策框架内使用 EA3 计算的综合证据评估来支持基于 RWE 的因果推论。我们还讨论了我们方法的特点和局限性，以及如何克服一些缺点。最后，我们展望了 RWE 的使用。
