title: Propensity scores used for analysis of cluster randomized trials with selection bias: a simulation study.
abstract: Cluster randomized trials (CRTs) are often prone to selection bias despite randomization. Using a simulation study, we investigated the use of propensity score (PS) based methods in estimating treatment effects in CRTs with selection bias when the outcome is quantitative. Of four PS-based methods (adjustment on PS, inverse weighting, stratification, and optimal full matching method), three successfully corrected the bias, as did an approach using classical multivariable regression. However, they showed poorer statistical efficiency than classical methods, with higher standard error for the treatment effect, and type I error much smaller than the 5% nominal level.
authors.affiliation: INSERM UMR-S 738, Paris, France. clemence.leyrat@univ-tours.fr
authors.forename: C
authors.initials: C
authors.lastname: Leyrat
authors.forename: A
authors.initials: A
authors.lastname: Caille
authors.forename: A
authors.initials: A
authors.lastname: Donner
authors.forename: B
authors.initials: B
authors.lastname: Giraudeau
mesh_terms: D000368:Aged; D016000:Cluster Analysis; D003198:Computer Simulation; D015444:Exercise / Q000502:physiology / Q000523:psychology; D005260:Female; D006801:Humans; D008297:Male; D010003:Osteoarthritis / Q000503:physiopathology / Q000628:therapy; D010146:Pain / Q000517:prevention & control; D057216:Propensity Score; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D015983:Selection Bias
keywords: Monte-Carlo simulations; cluster randomized trial; propensity score; selection bias