Quality of Life Research

, Volume 23, Issue 2, pp 459–466

Probabilistic mapping of the health status measure SF-12 onto the health utility measure EQ-5D using the US-population-based scoring models

Article

DOI: 10.1007/s11136-013-0517-3

Cite this article as:
Le, Q.A. Qual Life Res (2014) 23: 459. doi:10.1007/s11136-013-0517-3

Abstract

Purpose

Probabilistic mapping of the health status instrument SF-12 onto the health utility instrument EuroQol—5 dimensions (EQ-5D)-3L using the UK-population-based scoring model showed encouraging results as compared to other mapping methods, although its predictive performance using the US-population-based EQ-5D scoring models has not been investigated. In addition, a new and improved US-population-based EQ-5D scoring method has recently been developed and suggested for use in applications that required US societal health state values. In this study, we assessed predictive performance of the probabilistic mapping approach using the US-population-based scoring models on EQ-5D utility scores based on SF-12 responses and compared the results with those of other mapping methods.

Methods

Using a sample of 19,678 adults from the 2003 Medical Expenditure Panel Survey, we evaluated the predictive performance of probabilistic mapping using Bayesian networks, response mapping using multinomial logistic regression, ordinary least squares, and censored least absolute deviations models by implementing a fivefold cross-validation method. The EQ-5D utility scores were generated using two US-population-based models: D1 and MM-OC.

Results

Overall, the probabilistic mapping approach using Bayesian networks consistently outperformed other mapping methods with mean squared errors (MSE) of 0.007 and 0.007, mean absolute errors (MAE) of 0.057 and 0.039, and overall R2 of 0.773 and 0.770 for the US-population-based EQ-5D scoring D1 and MM-OC models, respectively.

Conclusion

The probabilistic mapping approach can be useful to estimate EQ-5D utility scores from SF-12 responses with better predictive measures in terms of MSE, MAE, and R2 than other common mapping methods.

Keywords

Health-related quality of life Health utility Probabilistic mapping Bayesian network SF-12 EQ-5D 

Supplementary material

11136_2013_517_MOESM1_ESM.doc (151 kb)
Supplementary material 1 (DOC 151 kb)

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Pharmacy Administration and PracticeWestern University of Health SciencesPomonaUSA

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