标题： 基于p值与原假设后验概率之间一致性的有效样本量估计 显示英文标题

标题： Effective sample size estimation based on concordance between p-value and posterior probability of the null hypothesis

摘要： 估计先验分布的有效样本量（ESS）是一个古老但关键的挑战，对临床试验和各种生物医学应用具有重要意义。 尽管已经进行了许多努力来解决这个问题，但大多数方法忽视了先验分布所嵌入的似然背景，从而将所有先验视为“有益的”。 在针对有害先验的有限研究中，指定一个基线先验仍然是不可或缺的步骤。 在本文中，通过p值与零假设后验概率之间的优雅桥梁，我们提出了一种基于假设检验框架中的p值的新ESS估计方法，在现有ESS估计方法的三个关键方面扩展了其适用范围： （i）我们考虑了先验的具体似然背景，使得在先验与似然不一致的情况下可能出现负的ESS值； （ii）通过利用非信息先验下频率学派和贝叶斯配置之间的已建立桥梁，无需指定一个基线先验，从而避免了另一个关于主观性的批评； （iii）通过将ESS纳入假设检验框架，我们的$p$-值ESS估计方法超越了传统的单ESS对应单先验范式，适应了单ESS对应多先验的范式，其中唯一的ESS可能反映多种先验在不同情境下的协同影响。 通过全面的模拟分析，我们展示了p值ESS估计方法相较于现有方法的优越性能。 此外，通过将该方法应用于表达数量性状位点（eQTL）数据的分析，我们展示了信息先验在揭示基因eQTL位点中的有效性。 显示英文摘要

摘要： Estimating the effective sample size (ESS) of a prior distribution is an age-old yet pivotal challenge, with great implications for clinical trials and various biomedical applications. Although numerous endeavors have been dedicated to this pursuit, most of them neglect the likelihood context in which the prior is embedded, thereby considering all priors as "beneficial". In the limited studies of addressing harmful priors, specifying a baseline prior remains an indispensable step. In this paper, by means of the elegant bridge between the p-value and the posterior probability of the null hypothesis, we propose a new ESS estimation method based on p-value in the framework of hypothesis testing, expanding the scope of existing ESS estimation methods in three key aspects: (i) We address the specific likelihood context of the prior, enabling the possibility of negative ESS values in case of prior-likelihood disconcordance; (ii) By leveraging the well-established bridge between the frequentist and Bayesian configurations under noninformative priors, there is no need to specify a baseline prior which incurs another criticism of subjectivity; (iii) By incorporating ESS into the hypothesis testing framework, our $p$-value ESS estimation method transcends the conventional one-ESS-one-prior paradigm and accommodates one-ESS-multiple-priors paradigm, where the sole ESS may reflect the collaborative impact of multiple priors in diverse contexts. Through comprehensive simulation analyses, we demonstrate the superior performance of the p-value ESS estimation method in comparison with existing approaches. Furthermore, by applying this approach to an expression quantitative trait loci (eQTL) data analysis, we show the effectiveness of informative priors in uncovering gene eQTL loci.