title: Identifying individual changes in performance with composite quality indicators while accounting for regression to the mean.
abstract: Almost a decade ago Morton and Torgerson indicated that perceived medical benefits could be due to "regression to the mean." Despite this caution, the regression to the mean "effects on the identification of changes in institutional performance do not seem to have been considered previously in any depth" (Jones and Spiegelhalter). As a response, Jones and Spiegelhalter provide a methodology to adjust for regression to the mean when modeling recent changes in institutional performance for one-variable quality indicators. Therefore, in our view, Jones and Spiegelhalter provide a breakthrough methodology for performance measures. At the same time, in the interests of parsimony, it is useful to aggregate individual quality indicators into a composite score. Our question is, can we develop and demonstrate a methodology that extends the "regression to the mean" literature to allow for composite quality indicators? Using a latent variable modeling approach, we extend the methodology to the composite indicator case. We demonstrate the approach on 4 indicators collected by the National Database of Nursing Quality Indicators. A simulation study further demonstrates its "proof of concept."
authors.affiliation: Department of Biostatistics, University of Kansas School of Medicine, Kansas City, Kansas (BJG)
authors.forename: Byron J
authors.initials: BJ
authors.lastname: Gajewski
authors.affiliation: University of Kansas School of Nursing, Kansas City, Kansas (ND)
authors.forename: Nancy
authors.initials: N
authors.lastname: Dunton
mesh_terms: D015999:Multivariate Analysis; D019984:Quality Indicators, Health Care; D012044:Regression Analysis