Shrinkage estimators for a composite measure of quality conceptualized as a formative construct.
OBJECTIVE
To demonstrate the value of shrinkage estimators when calculating a composite quality measure as the weighted average of a set of individual quality indicators.


METHODS
Rates of 28 quality indicators (QIs) calculated from the minimum dataset from residents of 112 Veterans Health Administration nursing homes in fiscal years 2005-2008.


METHODS
We compared composite scores calculated from the 28 QIs using both observed rates and shrunken rates derived from a Bayesian multivariate normal-binomial model.


RESULTS
Shrunken-rate composite scores, because they take into account unreliability of estimates from small samples and the correlation among QIs, have more intuitive appeal than observed-rate composite scores. Facilities can be profiled based on more policy-relevant measures than point estimates of composite scores, and interval estimates can be calculated without assuming the QIs are independent. Usually, shrunken-rate composite scores in 1 year are better able to predict the observed total number of QI events or the observed-rate composite scores in the following year than the initial year observed-rate composite scores.


CONCLUSIONS
Shrinkage estimators can be useful when a composite measure is conceptualized as a formative construct.
School of Management, Boston University, Boston, MA 02130, USA. mshwartz@bu.edu
Michael
M
Shwartz
Erol A
EA
Peköz
Cindy L
CL
Christiansen
James F
JF
Burgess
Dan
D
Berlowitz
D001499:Bayes Theorem; D006707:Homes for the Aged / Q000592:standards*; D006801:Humans; D009735:Nursing Homes / Q000592:standards*; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data*; D014481:United States; D014493:United States Department of Veterans Affairs / Q000592:standards*