Using Sensitivity Analyses for Unobserved Confounding to Address Covariate Measurement Error in Propensity Score Methods.

ABSTRACT: 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

SUBMITTER: Rudolph KE 

PROVIDER: S-EPMC5860046 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature