title: How Do Propensity Score Methods Measure Up in the Presence of Measurement Error? A Monte Carlo Study.
abstract: Considering that the absence of measurement error in research is a rare phenomenon and its effects can be dramatic, we examine the impact of measurement error on propensity score (PS) analysis used to minimize selection bias in behavioral and social observational studies. A Monte Carlo study was conducted to explore the effects of measurement error on the treatment effect and balance estimates in PS analysis across seven different PS conditioning methods. In general, the results indicate that even low levels of measurement error in the covariates lead to substantial bias in estimates of treatment effects and concomitant reduction in confidence interval coverage across all methods of conditioning on the PS.
authors.affiliation: a University of South Florida.
authors.forename: Patricia
authors.initials: P
authors.lastname: Rodríguez De Gil
authors.affiliation: a University of South Florida.
authors.forename: Aarti P
authors.initials: AP
authors.lastname: Bellara
authors.affiliation: a University of South Florida.
authors.forename: Rheta E
authors.initials: RE
authors.lastname: Lanehart
authors.affiliation: a University of South Florida.
authors.forename: Reginald S
authors.initials: RS
authors.lastname: Lee
authors.affiliation: a University of South Florida.
authors.forename: Eun Sook
authors.initials: ES
authors.lastname: Kim
authors.affiliation: a University of South Florida.
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Kromrey
mesh_terms: D035841:Behavioral Research / Q000379:methods*; D006801:Humans; D009010:Monte Carlo Method; D064887:Observational Studies as Topic / Q000379:methods*; D057216:Propensity Score; D015203:Reproducibility of Results; D015983:Selection Bias
keywords: Monte Carlo simulation; measurement error; propensity score methods