The R Journal: accepted article

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BayesSenMC: an R package for Bayesian Sensitivity Analysis of Misclassification PDF download
Jinhui Yang, Lifeng Lin and Haitao Chu

Abstract In case–control studies, the odds ratio is commonly used to summarize the association be tween 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 differen tial 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.

Received: ; online 2021-12-15, supplementary material, (1.4 Kb)
CRAN packages: BayesSenMC, episensr, lme4, rstan, ggplot2
CRAN Task Views implied by cited CRAN packages: Bayesian, Econometrics, Environmetrics, Graphics, OfficialStatistics, Phylogenetics, Psychometrics, SocialSciences, SpatioTemporal, TeachingStatistics


CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-097,
  author = {Jinhui Yang and Lifeng Lin and Haitao Chu},
  title = {{BayesSenMC: an R package for Bayesian Sensitivity Analysis
          of Misclassification}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-097},
  url = {https://journal.r-project.org/archive/2021/RJ-2021-097/index.html}
}