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.
BayesSenMC, episensr, lme4, rstan, ggplot2
Bayesian, Econometrics, Environmetrics, OfficialStatistics, Phylogenetics, Psychometrics, SocialSciences, SpatioTemporal, TeachingStatistics
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Yang, et al., "The R Journal: 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 = {The R Journal: 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}
}