Literature DB >> 11310943

Evaluating quality-adjusted life years: estimation of the health utility index (HUI2) from the SF-36.

M B Nichol1, N Sengupta, D R Globe.   

Abstract

UNLABELLED: Quality-adjusted life years (QALYs) are well recognized as a valid measure for outcomes in cost-effectiveness analyses. A summary health utility score is necessary to evaluate QALYs. The objective of this study was to predict a summary utility score (represented by the Health Utility Index [HUI2]) from scores on the SF-36.
METHODS: A structural equation framework was applied to longitudinal data collected from 1992 to 1995 on a sample of patients insured by Southem California Kaiser Permanente (N = 6921). An ordinary least squares (OLS) method was used to estimate the HUI2.
RESULTS: The OLS model on cross-sectional data predicted 50.5% of the observed variance in HUI2 scores. Parameter estimates of all SF-36 components showed statistical significance at the P < 0.05 level.
CONCLUSIONS: Results of this study provide a quantitative link between two important measures of health status. The present model can be used to estimate health utility summary scores in studies that have collected SF-36 data.

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Year:  2001        PMID: 11310943     DOI: 10.1177/0272989X0102100203

Source DB:  PubMed          Journal:  Med Decis Making        ISSN: 0272-989X            Impact factor:   2.583


  40 in total

1.  Estimation and comparison of derived preference scores from the SF-36 in lung transplant patients.

Authors:  Francis S Lobo; Cynthia R Gross; Barbara J Matthees
Journal:  Qual Life Res       Date:  2004-03       Impact factor: 4.147

2.  Estimating utilities for chronic kidney disease, using SF-36 and SF-12-based measures: challenges in a population of veterans with diabetes.

Authors:  Mangala Rajan; Kuan-Chi Lai; Chin-Lin Tseng; Shirley Qian; Alfredo Selim; Lewis Kazis; Leonard Pogach; Anushua Sinha
Journal:  Qual Life Res       Date:  2012-03-06       Impact factor: 4.147

3.  Estimating utility values for health states of overweight and obese individuals using the SF-36.

Authors:  Michael A Kortt; Philip M Clarke
Journal:  Qual Life Res       Date:  2005-12       Impact factor: 4.147

4.  Separating gains and losses in health when calculating the minimum important difference for mapped utility measures.

Authors:  Michael B Nichol; Joshua D Epstein
Journal:  Qual Life Res       Date:  2008-07-10       Impact factor: 4.147

5.  Predicting EQ-5D utility scores from the 25-item National Eye Institute Vision Function Questionnaire (NEI-VFQ 25) in patients with age-related macular degeneration.

Authors:  Nalin Payakachat; Kent H Summers; Andreas M Pleil; Matthew M Murawski; Joseph Thomas; Kristofer Jennings; James G Anderson
Journal:  Qual Life Res       Date:  2009-06-19       Impact factor: 4.147

6.  Deriving utility scores from the SF-36 health instrument using Rasch analysis.

Authors:  Graeme Hawthorne; Konstancja Densley; Julie F Pallant; Duncan Mortimer; Leonie Segal
Journal:  Qual Life Res       Date:  2008-09-30       Impact factor: 4.147

7.  Mapping analyses to estimate health utilities based on responses to the OM8-30 Otitis Media Questionnaire.

Authors:  Helen Dakin; Stavros Petrou; Mark Haggard; Sarah Benge; Ian Williamson
Journal:  Qual Life Res       Date:  2009-11-26       Impact factor: 4.147

Review 8.  Health, justice, and the environment.

Authors:  David B Resnik; Gerard Roman
Journal:  Bioethics       Date:  2007-05       Impact factor: 1.898

9.  Quality-of-life loss of people admitted to burn centers, United States.

Authors:  Ted Miller; Soma Bhattacharya; William Zamula; Dennis Lezotte; Karen Kowalske; David Herndon; James Fauerbach; Loren Engrav
Journal:  Qual Life Res       Date:  2012-12-08       Impact factor: 4.147

10.  Mapping CushingQOL scores to EQ-5D utility values using data from the European Registry on Cushing's syndrome (ERCUSYN).

Authors:  X Badia; M Roset; E Valassi; H Franz; A Forsythe; S M Webb
Journal:  Qual Life Res       Date:  2013-03-29       Impact factor: 4.147

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