Bayesian models for missing and misclassified variables using integrated nested Laplace approximations

TL;DR

This paper introduces a novel approach combining INLA with importance sampling to efficiently fit Bayesian models that correct for missing and misclassified variables, enhancing inference accuracy in regression analysis.

Contribution

It demonstrates how to implement Bayesian models for misclassification correction within the INLA framework, overcoming previous limitations related to latent categorical variables.

Findings

INLA combined with importance sampling effectively fits models with misclassified covariates.

The methods accurately correct for misclassification in real-world data applications.

The approach simplifies Bayesian inference for misclassified response variables without additional sampling.

Abstract

Misclassified variables used in regression models, either as a covariate or as the response, may lead to biased estimators and incorrect inference. Even though Bayesian models to adjust for misclassification error exist, it has not been shown how these models can be implemented using integrated nested Laplace approximation (INLA), a popular framework for fitting Bayesian models due to its computational efficiency. Since INLA requires the latent field to be Gaussian, and the Bayesian models adjusting for covariate misclassification error necessarily introduce a latent categorical variable, it is not obvious how to fit these models in INLA. Here, we show how INLA can be combined with importance sampling to overcome this limitation. We also discuss how to account for a misclassified response variable using INLA directly without any additional sampling procedure. The proposed methods are illustrated through a number of simulations and applications to real-world data, and all examples are presented with detailed code in the supporting information.