论文标题
参数设置中的广义报告方法的正式框架
A formal framework for generalized reporting methods in parametric settings
论文作者
论文摘要
旨在最大化统计模型结果的可解释性和可比性的效应大小测量和可视化技术长期以来一直非常重要，并且最近再次受到文献中的关注。但是，由于这种情况下提出的方法源于各种各样的学科，而且经常是实际动机，因此它们缺乏一个共同的理论框架，并且许多数量是狭窄或启发式定义的。在这项工作中，我们提出了旨在旨在参数回归结果的效果尺寸测量和可视化技术的常见数学设置，并为这种数量的现有和新变体的一致推导定义了正式框架。在整个介绍的理论中，我们利用概率措施来得出对感兴趣领域的加权手段。虽然我们采用贝叶斯方法来量化不确定性，以便为每个定义的数量得出一致的结果，但所有提出的方法都适用于频繁主义者和贝叶斯推论的结果。我们将从提出的框架得出的选定规范应用于临床试验和多分析师研究的数据，以说明其多功能性和相关性。
Effect size measures and visualization techniques aimed at maximizing the interpretability and comparability of results from statistical models have long been of great importance and are recently again receiving increased attention in the literature. However, since the methods proposed in this context originate from a wide variety of disciplines and are more often than not practically motivated, they lack a common theoretical framework and many quantities are narrowly or heuristically defined. In this work, we put forward a common mathematical setting for effect size measures and visualization techniques aimed at the results of parametric regression and define a formal framework for the consistent derivation of both existing and new variants of such quantities. Throughout the presented theory, we utilize probability measures to derive weighted means over areas of interest. While we take a Bayesian approach to quantifying uncertainty in order to derive consistent results for every defined quantity, all proposed methods apply to the results of both frequentist and Bayesian inference. We apply selected specifications derived from the proposed framework to data from a clinical trial and a multi-analyst study to illustrate its versatility and relevance.
