  In observational research treatment effects, the average treatment effect
(ATE) estimator may be biased if a confounding variable is misclassified. We
discuss the impact of classification error in a dichotomous confounding
variable in analyses using marginal structural models estimated using inverse
probability weighting (MSMs-IPW) and compare this with its impact in
conditional regression models, focusing on a point-treatment study with a
continuous outcome. Expressions were derived for the bias in the ATE estimator
from a MSM-IPW and conditional model by using the potential outcome framework.
Based on these expressions, we propose a sensitivity analysis to investigate
and quantify the bias due to classification error in a confounding variable in
MSMs-IPW. Compared to bias in the ATE estimator from a conditional model, the
bias in MSM-IPW can be dissimilar in magnitude but the bias will always be
equal in sign. A simulation study was conducted to study the finite sample
performance of MSMs-IPW and conditional models if a confounding variable is
misclassified. Simulation results showed that confidence intervals of the
treatment effect obtained from MSM-IPW are generally wider and coverage of the
true treatment effect is higher compared to a conditional model, ranging from
over coverage if there is no classification error to smaller under coverage
when there is classification error. The use of the bias expressions to inform a
sensitivity analysis was demonstrated in a study of blood pressure lowering
therapy. It is important to consider the potential impact of classification
error in a confounding variable in studies of treatment effects and a
sensitivity analysis provides an opportunity to quantify the impact of such
errors on causal conclusions. An online tool for sensitivity analyses was
developed: https://lindanab.shinyapps.io/SensitivityAnalysis.
