In epidemiology, a typical measure of interest is the risk of disease
conditional upon exposure. A common source of bias in the estimation of
risks and risk ratios is misclassification. Exposure misclassification
affects the measurement of exposure, i.e. the variable one conditions
on. This article explains how to assess biases under non-differential
exposure misclassification when estimating vaccine effectiveness, i.e.
the vaccine-induced relative reduction in the risk of disease. The
problem can be described in terms of three binary variables: the
unobserved true exposure status, the observed but potentially
misclassified exposure status, and the observed true disease status. The
bias due to exposure misclassification is quantified by the difference
between the naïve estimand defined as one minus the risk ratio comparing
individuals observed as vaccinated with individuals observed as
unvaccinated, and the vaccine effectiveness defined as one minus the
risk ratio comparing truly vaccinated with truly unvaccinated. The
magnitude of the bias depends on five factors: the risks of disease in
the truly vaccinated and the truly unvaccinated, the sensitivity and
specificity of exposure measurement, and vaccination coverage.
Non-differential exposure misclassification bias is always negative. In
practice, if the sensitivity and specificity are known or estimable from
external sources, the true risks and the vaccination coverage can be
estimated from the observed data and, thus, the estimation of vaccine
effectiveness based on the observed risks can be corrected for exposure
misclassification. When analysing risks under misclassification, careful
consideration of conditional probabilities is crucial.