The R Journal: article published in 2021, volume 13:2

BayesSenMC: an R package for Bayesian Sensitivity Analysis of Misclassification
Jinhui Yang, Lifeng Lin and Haitao Chu , The R Journal (2021) 13:2, pages 228-238.

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: 2020-06-05; online 2021-12-15, supplementary material, (1.4 KiB)
CRAN packages: BayesSenMC, episensr, lme4, rstan, ggplot2
CRAN Task Views implied by cited CRAN packages: Bayesian, Econometrics, Environmetrics, OfficialStatistics, Phylogenetics, Psychometrics, SocialSciences, SpatioTemporal, TeachingStatistics

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://doi.org/10.32614/RJ-2021-097},
  pages = {228--238},
  volume = {13},
  number = {2}
}