BayesSenMC: an R package for Bayesian Sensitivity Analysis of Misclassification

Jinhui Yang, Lifeng Lin, Haitao Chu

Research output: Contribution to journalArticlepeer-review


In case–control studies, the odds ratio is commonly used to summarize the association between 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 differential 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.

Original languageEnglish (US)
Pages (from-to)228-238
Number of pages11
JournalR Journal
Issue number2
StatePublished - 2021

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