• cost-effectiveness;
  • simulation;
  • validation data

Unmeasured confounding is a common problem in observational studies. Failing to account for unmeasured confounding can result in biased point estimators and poor performance of hypothesis tests and interval estimators. We provide examples of the impacts of unmeasured confounding on cost-effectiveness analyses using observational data along with a Bayesian approach to correct estimation. Assuming validation data are available, we propose a Bayesian approach to correct cost-effectiveness studies for unmeasured confounding. We consider the cases where both cost and effectiveness are assumed to have a normal distribution and when costs are gamma distributed and effectiveness is normally distributed. Simulation studies were conducted to determine the impact of ignoring the unmeasured confounder and to determine the size of the validation data required to obtain valid inferences. Copyright © 2013 John Wiley & Sons, Ltd.