Developing a New Version of the SF-6D Health State Classification System From the SF-36v2: SF-6Dv2.
Adolescent
Adult
Aged
Cost-Benefit Analysis
/ methods
Cross-Sectional Studies
Cultural Competency
Factor Analysis, Statistical
Female
Health Status
Humans
Male
Mental Health
Middle Aged
Pain
/ epidemiology
Physical Functional Performance
Psychometrics
Quality-Adjusted Life Years
Reproducibility of Results
Social Participation
Socioeconomic Factors
Surveys and Questionnaires
/ standards
Young Adult
Journal
Medical care
ISSN: 1537-1948
Titre abrégé: Med Care
Pays: United States
ID NLM: 0230027
Informations de publication
Date de publication:
06 2020
06 2020
Historique:
entrez:
16
5
2020
pubmed:
16
5
2020
medline:
11
7
2020
Statut:
ppublish
Résumé
The objective of this study was to develop the classification system for version of the SF-6D (SF-6Dv2) from the SF-36v2. SF-6Dv2 is an improved version of SF-6D, one of the most widely used generic measures of health for the calculation of quality-adjusted life years. A 3-step process was undertaken to generate a new classification system: (1) factor analysis to establish dimensionality; (2) Rasch analysis to understand item performance; and (3) tests of differential item function. To evaluate robustness, Rasch analyses were performed in multiple subsets of 2 large cross-sectional datasets from recently discharged hospital patients and online patient samples. On the basis of factor analysis, other psychometric evidence, cross-cultural considerations, and amenability to valuation, the 6-dimension classification used in SF-6D was maintained. SF-6Dv2 resulted in the following modifications to SF-6D: a simpler classification of physical function with clearer separation between levels; a more detailed 5-level description of role limitations; using negative wording to describe vitality; and using pain severity rather than pain interference. The SF-6Dv2 classification system describes more distinct levels of health than SF-6D, changes the descriptions used for a number of dimensions and provides clearer wording for health state valuation. The second stage of the study has developed a utility value set using discrete choice methods so that the measure can be used in health technology assessment. Further work should investigate the psychometric characteristics of the new instrument.
Identifiants
pubmed: 32412942
doi: 10.1097/MLR.0000000000001325
pii: 00005650-202006000-00009
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
557-565Références
Brazier J, Roberts J, Deverill M. The estimation of a preference-based measure of health from the SF-36. J Health Econ. 2002;21:271–292.
Brazier JE, Roberts J. Estimating a preference-based index from the SF-12. Med Care. 2004;42:851–859.
Ware JE, Sherbourne CD. The MOS 36-Item Short-Form Health Survey (SF-36): I. Conceptual Framework and Item Selection. Med Care. 1992;30:473–483.
Wisløff T, Hagen G, Hamidi V, et al. Estimating QALY gains in applied studies: a review of cost utility analyses published in 2010. Pharmacoeconomics. 2014;32:367–375.
Abellán Perpiñán JM, Sánchez Martínez FI, Martínez Pérez JE, et al. Lowering the ‘floor’ of the SF-6D scoring algorithm using a lottery equivalent method. Health Econ. 2012;21:1271–1285.
Brazier J, Fukuhara S, Roberts J, et al. Estimating a preference-based index from the Japanese SF-36. J Clin Epidemiol. 2009;62:1323–1331.
Cruz L, Camey S, Hoffmann JF, et al. Estimating the SF-6D value set for a population-based sample of Brazilians. Value Health. 2011;14:S108–S114.
Ferreira LN, Ferreira PL, Pereira LN, et al. A Portuguese value set for the SF-6D. Value Health. 2010;13:624–630.
Jonker MF, Donkers B, de Bekker-Grob EW, et al. Advocating a paradigm shift in health-state valuations: the estimation of time-preference corrected QALY Tariffs. Value Health. 2018;21:993–1001.
Lam CL, Brazier J, McGhee SM. Valuation of the SF-6D health states is feasible, acceptable, reliable, and valid in a Chinese population. Value Health. 2008;11:295–303.
McGhee SM, Brazier J, Lam CL, et al. Quality-adjusted life years: population-specific measurement of the quality component. Hong Kong Med J. 2011;17:17–21.
Norman R, Viney R, Brazier J, et al. Valuing SF-6D health states using a discrete choice experiment. Med Decis Making. 2014;34:773–786.
International Society for Pharmacoeconomics & Outcomes Research. Pharmacoeconomic guidelines around the world [Online]. 2019 Available at: https://tools.ispor.org/peguidelines/. Accessed May 5, 2019.
Brazier JE, Connell J, Papaioannou D, et al. Validating generic preference-based measures of health in mental health populations and estimating mapping functions for widely used specific measures. Health Technol Assess. 2014;18:1–188.
Mulhern B, Mukuria C, Barkham M, et al. Using preference-based measures in mental health conditions: the psychometric validity of the EQ-5D and SF-6D. Br J Psychiatry. 2014;205:236–243.
Longworth L, Yang Y, Young T, et al. Use of generic and condition specific measures of Health Related Quality of Life in NICE decision making. Health Technol Assess. 2014;18:1–224.
Brazier JE, Tsuchiya A, Roberts J, et al. A comparison of the EQ-5D and the SF-6D across seven patient groups. Health Econ. 2004;13:873–884.
Ferreira PL, Ferreira LN, Pereira LN. How consistent are health utility values? Qual Life Res. 2008;17:1031–1042.
Longworth L, Bryan S. An empirical comparison of EQ-5D and SF-6D in liver transplant patients. Health Econ. 2003;12:1061–1067.
Ware JE, Kosinski M, Dewey JE. How to Score Version Two of the SF-36 Health Survey. Lincoln, RI: QualityMetric Incorporated; 2000.
Ware JE, Kosinski M, Bjorner JB, et al. User’s Manual for the SF 36v2® Health Survey, 2nd ed. Lincoln, RI: QualityMetric Incorporated; 2007.
McCabe C, Brazier J, Gilks P, et al. Using rank data to estimate health state utility models. J Health Econ. 2006;25:418–431.
Kharroubi SA, Brazier JE, Roberts J, et al. Modelling SF-6D health state preference data using a nonparametric Bayesian method. J Health Econ. 2007;26:597–612.
Mulhern B, Norman R, Bansback N, et al. Valuing SF-6Dv2 in the UK using a discrete choice experiment with duration. Med Care. 2020. Doi: 10.1097/MLR.0000000000001324.
doi: 10.1097/mlr.0000000000001324
Brazier JE, Rowen D, Mavranezouli I, et al. Developing and testing methods for deriving preference-based measures of health from condition specific measures (and other patient based measures of outcome). Health Technol Assess. 2012;16:1–114.
Mulhern B, Rowen D, Jacoby A, et al. The development of a QALY measure for epilepsy: NEWQOL-6D. Epilepsy Behav. 2012;24:36–43.
Rowen D, Brazier J, Young T, et al. Deriving a preference based measure for cancer using the EORTC QLQ-C30. Value Health. 2011;14:721–731.
Frendl DM, Ware JE. Patient-reported functional health and well-being outcomes with drug therapy: a systematic review of randomized trials using the SF-36 health survey. Med Care. 2014;52:439–445.
Currie CJ, McEwan P, Peters JR, et al. The routine collation of health outcomes data from hospital treated subjects in the Health Outcomes Data Repository (HODaR): descriptive analysis from the first 20,000 subjects. Value Health. 2005;8:581–590.
Richardson J, Iezzi A, Maxwell A. Cross-national Comparison of Twelve Quality of Life Instruments: MIC Paper 1 Background, Questions, Instruments. Melbourne, Australian: Centre for Health Economics, Monash University; 2012.
De Vet HC, Ader HJ, Terwee CB, et al. Are factor analytical techniques used appropriately in the validation of health status questionnaires? A systematic review on the quality of factor analysis of the SF-36. Qual Life Res. 2005;14:1203–1218.
McHorney CA, Ware JE, Lu JF, et al. The MOS 36-item Short-Form Health Survey (SF-36): III. Tests of data quality, scaling assumptions, and reliability across diverse patient groups. Med Care. 1994;32:40–66.
Keller SD, Ware JE, Bentler PM, et al. Use of structural equation modeling to test the construct validity of the SF-36 Health Survey in ten countries: results from the IQOLA Project. International Quality of Life Assessment. J Clin Epidemiol. 1998;51:1179–1188.
Ware JE, Kosinski M, Gandek B, et al. The factor structure of the SF-36 Health Survey in 10 countries: results from the IQOLA Project. International Quality of Life Assessment. J Clin Epidemiol. 1998;51:1159–1165.
Bjorner JB, Ware JE, Kosinski M. The potential synergy between cognitive models and modern psychometric models. Qual Life Res. 2003;12:261–274.
Deng N, Rick G, Ware JE. Energy, fatigue, or both? A bifactor modeling approach to the conceptualization and measurement of vitality. Qual Life Res. 2015;24:81–93.
Suzukamoa Y, Fukuhara S, Green J, et al. Validation testing of a three-component model of Short Form-36 scores. J Clin Epidemiol. 2011;64:301–308.
StataCorp. Stata Statistical Software: Release 15. College Station, TX: StataCorp LP; 2011.
Floyd FJ, Widaman KF. Factor analysis in the development and refinement of clinical assessment instruments. Psychol Assess. 1995;7:286–299.
Hu LT, Bentler PM. Cutoff criteria for fit indices in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling. 1999;6:1–55.
Kline RB. Principles and Practice of Structural Equation Modeling, 2nd ed. New York, NY: Guilford; 2005.
Bentler PM. Comparative fit indexes in structural models. Psychol Bull. 1990;107:238–246.
Tucker LR, Lewis C. A reliability coefficient for maximum likelihood factor analysis. Psychometrika. 1973;38:1–10.
Okonkwo O, Roth D, Pulley L, et al. Confirmatory factor analysis of the validity of the SF-12 for persons with and without a history of stroke. Qual Life Res. 2010;19:1323–1331.
Su CT, Ng HS, Yang AL, et al. Psychometric Evaluation of the Short Form 36 Health Survey (SF-36) and the World Health Organization Quality of Life Scale Brief Version (WHOQOL-BREF) for Patients With Schizophrenia. Psychol Assess. 2014;26:980–989.
Rasch G. Probabilistic Model for Some Intelligence and Achievement Tests. Copenhagen, Denmark: Danish Institute for Educational Research; 1960.
Andrich D, Lyne A, Sheridan B, et al. RUMM 2030. Perth: RUMM Laboratory; 2010.
Hagquist C, Andrich D. Recent advances in analysis of differential item functioning in health research using the Rasch model. Health Qual Life Outcomes. 2017;15:181.
Linacre JM. Sample size and item calibration stability. Rasch Meas Trans. 1994;7:328.
Burkner PC, Schulte N, Holling H. On the statistical and practical limitations of Thurstonian IRT models. Educ Psychol Meas. 2019;79:827–854.
Wailoo AJ, Hernandez-Alava M, Manca A, et al. Mapping to estimate health-state utility from non–preference-based outcome measures: an ISPOR Good Practices for Outcomes Research Task Force Report. Value Health. 2017;20:18–27.
Herdman M, Gudex C, Lloyd A, et al. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual Life Res. 2011;20:1727–1736.
Devlin N, Shah K, Feng Y, et al. Valuing Health-Related Quality of Life: An EQ-5D-5L Value Set for England. Health Econ. 2018;27:7–22.