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
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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} }