BayesSenMC: an R package for Bayesian Sensitivity Analysis of Misclassification

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.

Jinhui Yang , Lifeng Lin , Haitao Chu
2021-12-15

CRAN packages used

BayesSenMC, episensr, lme4, rstan, ggplot2

CRAN Task Views implied by cited packages

Bayesian, Econometrics, Environmetrics, OfficialStatistics, Phylogenetics, Psychometrics, SocialSciences, SpatioTemporal, TeachingStatistics

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Citation

For attribution, please cite this work as

Yang, et al., "BayesSenMC: an R package for Bayesian Sensitivity Analysis of Misclassification", The R Journal, 2021

BibTeX citation

@article{RJ-2021-097,
  author = {Yang, Jinhui and Lin, Lifeng and Chu, Haitao},
  title = {BayesSenMC: an R package for Bayesian Sensitivity Analysis of Misclassification},
  journal = {The R Journal},
  year = {2021},
  note = {https://doi.org/10.32614/RJ-2021-097},
  doi = {10.32614/RJ-2021-097},
  volume = {13},
  issue = {2},
  issn = {2073-4859},
  pages = {228-238}
}