Propensity score methods are a popular tool with which to control for confounding in observational data, but their bias-reduction properties - as well as internal validity, generally - are threatened by covariate measurement error. There are few easy-to-implement methods of correcting for such bias. In this paper, we describe and demonstrate how existing sensitivity analyses for unobserved confounding - propensity score calibration, VanderWeele and Arah's bias formulas, and Rosenbaum's sensitivity analysis - can be adapted to address this problem. In a simulation study, we examine the extent to which these sensitivity analyses can correct for several measurement error structures: classical, systematic differential, and heteroscedastic covariate measurement error. We then apply these approaches to address covariate measurement error in estimating the association between depression and weight gain in a cohort of adults in Baltimore, Maryland. We recommend the use of VanderWeele and Arah's bias formulas and propensity score calibration (assuming it is adapted appropriately for the measurement error structure), as both approaches perform well for a variety of propensity score estimators and measurement error structures.
- confounding factors (epidemiology)
- measurement error
- propensity score
- unobserved confounding
ASJC Scopus subject areas