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Propensity scores used for analysis of cluster randomized trials with selection bias: a simulation study
Author(s) -
Leyrat C.,
Caille A.,
Donner A.,
Giraudeau B.
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5795
Subject(s) - propensity score matching , crts , inverse probability weighting , statistics , selection bias , weighting , regression , causal inference , selection (genetic algorithm) , type i and type ii errors , matching (statistics) , randomization , econometrics , randomized controlled trial , computer science , mathematics , medicine , artificial intelligence , computer graphics (images) , radiology
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. Copyright © 2013 John Wiley & Sons, Ltd.