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Joint misclassification of exposure and outcome variables can lead toconsiderable bias in epidemiological studies of causal exposure-outcomeeffects. In this paper, we present a new maximum likelihood based estimator forthe marginal causal odd-ratio that simultaneously adjusts for confounding andseveral forms of joint misclassification of the exposure and outcome variables.The proposed method relies on validation data for the construction of weightsthat account for both sources of bias. The weighting estimator, which is anextension of the exposure misclassification weighting estimator proposed byGravel and Platt (Statistics in Medicine, 2018), is applied to reinfarctiondata. Simulation studies were carried out to study its finite sample propertiesand compare it with methods that do not account for confounding ormisclassification. The new estimator showed favourable large sample propertiesin the simulations. Further research is needed to study the sensitivity of theproposed method and that of alternatives to violations of their assumptions.The implementation of the estimator is facilitated by a new R function in anexisting R package.

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