Correcting Misclassified Binary Outcomes in Association Studies

Use frequentist and Bayesian methods to estimate parameters from a binary outcome misclassification model. These methods correct for the problem of "label switching" by assuming that the sum of outcome sensitivity and specificity is at least 1. A description of the analysis methods is available in Hochstedler and Wells (2023) .

COMBO

COMBO: COrrecting Misclassified Binary Outcomes

Overview

COMBO provides a set of functions for the analysis of regression models with binary outcome misclassification.

The two main parts are:

• Classification probability calculations
• Parameter estimation

Classification probability calculations

The package allows users to compute the probability of the latent true outcome and the conditional probability of observing an outcome given the latent true outcome, based on parameters estimated from the COMBO_EM and COMBO_MCMC functions.

Parameter estimation

Jointly estimate parameters from the true outcome and observation mechanisms, respectively, in a binary outcome misclassification model using the EM algorithm or MCMC. Parameters from the true outcome, first-stage observation, and second-stage observation mechanisms in a two-stage binary outcome misclassification model can also be estimated using the EM algorithm and MCMC.

Installation

# Install from CRAN
install.packages("COMBO")

# Install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("kimberlywebb/COMBO")

Please note that COMBO requires JAGS to be installed. JAGS can be downloaded from https://sourceforge.net/projects/mcmc-jags/.