与功率有关的随机变量的分布(及其在临床试验中的应用)

IF 1.2 3区 数学 Q2 STATISTICS & PROBABILITY
Francesco Mariani, Fulvio De Santis, Stefania Gubbiotti
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引用次数: 0

摘要

在贝叶斯-频率主义混合假设检验方法中,幂函数(即拒绝零假设的概率)是一个随机变量,通常通过所谓的成功概率(PoS)对研究进行实验前评估。PoS 通常被定义为随机幂的期望值,它不一定是整个分布的代表性总结。在此,我们考虑了 PoS 的主要定义,并研究了引起 PoS 的与功率相关的随机变量。我们提供了它们的累积分布和概率密度函数的一般表达式,以及当检验统计量至少在渐近上是正态时的闭式表达式。对这些分布的分析凸显了 PoS 主要定义中的差异,使我们更倾向于基于检验效用函数的定义。我们通过一个例子来说明我们的想法,并将其应用到临床试验中,临床试验是常用 PoS 的框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The distribution of power-related random variables (and their use in clinical trials)

The distribution of power-related random variables (and their use in clinical trials)

In the hybrid Bayesian-frequentist approach to hypotheses tests, the power function, i.e. the probability of rejecting the null hypothesis, is a random variable and a pre-experimental evaluation of the study is commonly carried out through the so-called probability of success (PoS). PoS is usually defined as the expected value of the random power that is not necessarily a well-representative summary of the entire distribution. Here, we consider the main definitions of PoS and investigate the power related random variables that induce them. We provide general expressions for their cumulative distribution and probability density functions, as well as closed-form expressions when the test statistic is, at least asymptotically, normal. The analysis of such distributions highlights discrepancies in the main definitions of PoS, leading us to prefer the one based on the utility function of the test. We illustrate our idea through an example and an application to clinical trials, which is a framework where PoS is commonly employed.

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来源期刊
Statistical Papers
Statistical Papers 数学-统计学与概率论
CiteScore
2.80
自引率
7.70%
发文量
95
审稿时长
6-12 weeks
期刊介绍: The journal Statistical Papers addresses itself to all persons and organizations that have to deal with statistical methods in their own field of work. It attempts to provide a forum for the presentation and critical assessment of statistical methods, in particular for the discussion of their methodological foundations as well as their potential applications. Methods that have broad applications will be preferred. However, special attention is given to those statistical methods which are relevant to the economic and social sciences. In addition to original research papers, readers will find survey articles, short notes, reports on statistical software, problem section, and book reviews.
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