多组双侧数据的风险差异的贝叶斯推断
Bayesian inference of risk differences for multi-group bilateral data
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中文总结 AI 辅助
本文提出一个贝叶斯框架用于多组双侧数据的风险差异推断，推导三种客观先验并设计后验检验程序，在模拟和真实数据中优于频率学派方法，尤其在小样本和稀疏数据下。
中文摘要 AI 辅助
来自成对身体部位的双侧数据在临床研究中常见且具有相关性。经典频率学派方法在小样本或稀疏数据集中表现不佳。本文在Dallal模型下为多组双侧数据开发了一个贝叶斯框架。我们推导了三种客观先验（均匀先验、Jeffreys先验和Bernardo参考先验），并提出了一种基于范围的贝叶斯后验检验程序，结合决策规则，用于检验风险差异的同质性，并校准了等价性边界。蒙特卡洛模拟评估了经验第一类错误率、功效和区间估计性质。结果表明，贝叶斯方法实现了准确的覆盖率、更窄的置信区间，并且比频率学派的Wald方法更好地控制了第一类错误，尤其是在小样本和稀疏数据设置中。我们用两个真实数据集说明了该方法。所提出的框架为多组双侧数据分析提供了稳健且灵活的工具。
英文摘要
Bilateral data from paired body parts are common and correlated in clinical studies. Classical frequentist methods perform poorly in small or sparse datasets. This paper develops a Bayesian framework for bilateral data with multiple groups under Dallal's model. We derive three objective priors (uniform, Jeffreys', and Bernardo's reference priors) and propose a range-based posterior testing procedure, combined with a decision rule, to test the homogeneity of risk differences, with the equivalence margin calibrated. Monte Carlo simulations evaluate empirical Type I error rates, powers, and interval estimation properties. Results show that the Bayesian methods achieve accurate coverage probabilities, narrower confidence intervals, and better Type I error control than the frequentist Wald approach, especially in small-sample and sparse-data settings. We illustrate the methodology with two real datasets. The proposed framework provides a robust and flexible tool for multi-group bilateral data analysis.
发表机构
- College of Mathematics and System Science, Xinjiang University（新疆大学数学与系统科学学院）
机构由 AI 辅助整理，请以论文原文为准。
