Literature DB >> 15090102

Predicting EuroQoL EQ-5D preference scores from the SF-12 Health Survey in a nationally representative sample.

William F Lawrence1, John A Fleishman.   

Abstract

PURPOSE: To predict the EuroQoL EQ-5D utility index from the SF-12 Health Survey for a US national sample of adults.
METHODS: The authors used the 2000 Medical Expenditure Panel Survey to examine the relationship between instruments. Linear regression was used to predict EQ-5D scores from Physical Component Summary (PCS) and Mental Component Summary (MCS) scores of the SF-12. A prediction model was derived in one half of the sample and validated in the other half.
RESULTS: Complete responses to both measures were available for 14,580 adults; 7313 (50.2%) surveys were used for the derivation set. The 2-variable model predicted 61% of the variance in EQ-5D scores and provided reasonable ability to predict mean EQ-5D scores from mean PCS and MCS scores. Confidence intervals are dependent on sample size and variance of PCS and MCS scores.
CONCLUSIONS: EQ-5D scores can be reasonably predicted from the SF-12. This model allows researchers to estimate utility data for use in decision and cost-utility analyses.

Entities:  

Mesh:

Year:  2004        PMID: 15090102     DOI: 10.1177/0272989X04264015

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


  39 in total

1.  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

2.  Mapping the EQ-5D index from the SF-12: US general population preferences in a nationally representative sample.

Authors:  Patrick W Sullivan; Vahram Ghushchyan
Journal:  Med Decis Making       Date:  2006 Jul-Aug       Impact factor: 2.583

3.  Measuring preferences for cost-utility analysis: how choice of method may influence decision-making.

Authors:  Christine M McDonough; Anna N A Tosteson
Journal:  Pharmacoeconomics       Date:  2007       Impact factor: 4.981

4.  US and UK versions of the EQ-5D preference weights: does choice of preference weights make a difference?

Authors:  I-Chan Huang; Richard J Willke; Mark J Atkinson; William R Lenderking; Constantine Frangakis; Albert W Wu
Journal:  Qual Life Res       Date:  2007-04-06       Impact factor: 4.147

5.  Mapping the eight-item Parkinson's Disease Questionnaire (PDQ-8) to the EQ-5D utility index.

Authors:  Y B Cheung; L C S Tan; P N Lau; W L Au; N Luo
Journal:  Qual Life Res       Date:  2008-09-20       Impact factor: 4.147

6.  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

7.  Predicting preference-based SF-6D index scores from the SF-8 health survey.

Authors:  P Wang; A Z Fu; H L Wee; J Lee; E S Tai; J Thumboo; N Luo
Journal:  Qual Life Res       Date:  2012-10-10       Impact factor: 4.147

8.  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

9.  Predicting an SF-6D preference-based score using MCS and PCS scores from the SF-12 or SF-36.

Authors:  Janel Hanmer
Journal:  Value Health       Date:  2009-03-24       Impact factor: 5.725

10.  Predicting EuroQol (EQ-5D) scores from the patient-reported outcomes measurement information system (PROMIS) global items and domain item banks in a United States sample.

Authors:  Dennis A Revicki; Ariane K Kawata; Neesha Harnam; Wen-Hung Chen; Ron D Hays; David Cella
Journal:  Qual Life Res       Date:  2009-05-27       Impact factor: 4.147

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