Literature DB >> 18181221

Statistical methodology for classifying units on the basis of multiple-related measures.

Armando Teixeira-Pinto1, Sharon-Lise T Normand.   

Abstract

Both the private and public sectors have begun giving financial incentives to healthcare providers, such as hospitals, delivering superior 'quality of care'. Quality of care is assessed through a set of disease-specific measures that characterize the performance of healthcare providers. These measures are then combined into a unidimensional composite score. Most of the programs that reward superior performance use raw averages of the measures as the composite score. The scores based on raw averages fail to take into account typical characteristics of data used for performance evaluation, such as within-patient and within-hospital correlations, variable number of measures available in different hospitals, and missing data. In this paper, we contrast two different versions of composites based on raw average scores with a model-based score constructed using a latent variable model. We also present two methods to identify hospitals with superior performance. The methods are illustrated using national data collected to evaluate quality of care delivered by the U.S. acute care hospitals. 2008 John Wiley & Sons, Ltd

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Year:  2008        PMID: 18181221      PMCID: PMC2278020          DOI: 10.1002/sim.3187

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  18 in total

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8.  High-dimensional multivariate probit analysis.

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Journal:  Biometrics       Date:  1996-12       Impact factor: 2.571

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  11 in total

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5.  An econometric approach to aggregating multiple cardiovascular outcomes in German hospitals.

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10.  Latent composite indicators for evaluating adherence to guidelines in patients with a colorectal cancer diagnosis.

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