BayesSenMC:一个用于误分类贝叶斯敏感性分析的R包

R J. Pub Date : 2021-01-01 DOI:10.32614/rj-2021-097
Jinhui Yang, Lifeng Lin, H. Chu
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引用次数: 0

摘要

在病例对照研究中,比值比通常用于总结二元暴露与二元结果之间的关系。然而,由于不准确的数据报告,暴露错误分类经常出现在病例对照研究中,这可能在关联测量中产生偏差。在本文中,我们实现了错误分类的贝叶斯灵敏度分析,以提供对非微分和微分错误分类下校正的优势比的完整后验推断。我们提出了一个R (R Core Team, 2018)包BayesSenMC,它为其实现提供了用户友好的功能。这种用法是通过对双相情感障碍和类风湿性关节炎之间关联的真实数据分析来说明的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BayesSenMC: an R package for Bayesian Sensitivity Analysis of Misclassification
In case–control studies, the odds ratio is commonly used to summarize the association between a binary exposure and a dichotomous outcome. However, exposure misclassification frequently appears in case–control studies due to inaccurate data reporting, which can produce bias in measures of association. In this article, we implement a Bayesian sensitivity analysis of misclassification to provide a full posterior inference on the corrected odds ratio under both non-differential and differential misclassification. We present an R (R Core Team, 2018) package BayesSenMC, which provides user-friendly functions for its implementation. The usage is illustrated by a real data analysis on the association between bipolar disorder and rheumatoid arthritis.
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