# A weighting method for simultaneous adjustment for confounding and joint exposure-outcome misclassifications

## TL;DR

This paper introduces a new maximum likelihood estimator that adjusts for confounding and joint misclassification in epidemiological studies, improving causal effect estimates using validation data and a novel R implementation.

## Contribution

It presents a novel weighting estimator extending previous methods to simultaneously correct for confounding and joint misclassification, with demonstrated favorable properties in simulations.

## Key findings

- Favorable large sample properties in simulations
- Effective adjustment for confounding and misclassification
- Implementation available via an R package

## Abstract

Joint misclassification of exposure and outcome variables can lead to considerable bias in epidemiological studies of causal exposure-outcome effects. In this paper, we present a new maximum likelihood based estimator for the marginal causal odd-ratio that simultaneously adjusts for confounding and several forms of joint misclassification of the exposure and outcome variables. The proposed method relies on validation data for the construction of weights that account for both sources of bias. The weighting estimator, which is an extension of the exposure misclassification weighting estimator proposed by Gravel and Platt (Statistics in Medicine, 2018), is applied to reinfarction data. Simulation studies were carried out to study its finite sample properties and compare it with methods that do not account for confounding or misclassification. The new estimator showed favourable large sample properties in the simulations. Further research is needed to study the sensitivity of the proposed method and that of alternatives to violations of their assumptions. The implementation of the estimator is facilitated by a new R function in an existing R package.