  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.
