Shrinkage estimators for a composite measure of quality conceptualized as a formative construct.

Abstract:

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. DATA SOURCES: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. STUDY DESIGN:We compared composite scores calculated from the 28 QIs using both observed rates and shrunken rates derived from a Bayesian multivariate normal-binomial model. PRINCIPAL FINDINGS: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. CONCLUSION:Shrinkage estimators can be useful when a composite measure is conceptualized as a formative construct.

journal_name

Health Serv Res

journal_title

Health services research

authors

Shwartz M,Peköz EA,Christiansen CL,Burgess JF Jr,Berlowitz D

doi

10.1111/j.1475-6773.2012.01437.x

subject

Has Abstract

pub_date

2013-02-01 00:00:00

pages

271-89

issue

1

eissn

0017-9124

issn

1475-6773

journal_volume

48

pub_type

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