Deriving a mapping algorithm for converting SF-36 scores to EQ-5D utility score in a Korean population

Health and Quality of Life Outcomes

Min-Woo Jo²  Sang-il Lee²  Seon-Ok Kim³  Seon-Ha Kim¹ 

[1] Department of Nursing, Dankook University, 119 Dandaero, Cheonan 330-714, South Korea;Department of Preventive Medicine, University of Ulsan College of Medicine, 86, Asanbyeongwon-gil, Songpa-gu 138-736, Seoul, South Korea;Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, 86, Asanbyeongwon-gil, Songpa-gu 138-736, Seoul, South Korea

关键词: Korea;    Utility;    Quality of life;    SF-36;    EQ-5D;   

Others  :  1164518
DOI  :  10.1186/s12955-014-0145-9

 received in 2014-03-20, accepted in 2014-09-12,  发布年份 2014

【 摘 要 】

Background

There is no research on mapping algorithms between EQ-5D and SF-36 in Korea. The aim of this study was to derive a predictive model for converting the SF-36 health profile to the EQ-5D index using data from several studies.

Methods

Individual data (n?=?2211) were collected from three different studies and separated into derivation (n?=?1660) and internal validation sets (n?=?551). Data from 123 colon cancer patients were analyzed for external validation. The prediction models were analyzed using ordinary least-square (OLS) regression, two-part modeling, and multinomial logistic modeling using eight scale scores; two summary scores and the interaction terms of SF-36 were used as independent variables. The EQ-5D index using the Korean value set and each dimension of the EQ-5D were used as dependent variables. The mean absolute errors (MAE) and R² values of the internal and external validation dataset were used to evaluate model performance.

Results

Our findings show that the three different scoring algorithms demonstrate similar performances in terms of MAE and R². After considering familiarity and parsimony, the OLS model (including Physical Function, Bodily Pain, Social Function, Role Emotional, and Mental Health) was found to be optimal as the final algorithm for use in this study. The MAEs of the OLS models demonstrated consistent results in both the derivation (0.087¿0.109) and external validation sets (0.082¿0.097).

Conclusion

This study provides mapping algorithms for estimating the EQ-5D index from the SF-36 profile using individual data and confirms that these algorithms demonstrate high explanatory power and low prediction errors.

【 授权许可】

   
2014 Kim et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

【 参考文献 】

• [1]Drummond MF, Sculpher MJ, Torrance GW: Methods for the economic evaluation of health care programs. Oxford University Press, UK; 2005.
• [2]Dolan P, Gudex C, Kind P, Williams A: Valuing health states: a comparison of methods. J Health Econ 1996, 15:209-231.
• [3]Torrance GW: Measurement of health state utilities for economic appraisal. J Health Econ 1986, 5:1-30.
• [4]Torrance GW, Feeny DH, Furlong WJ, Barr RD, Zhang Y, Wang Q: Multiattribute utility function for a comprehensive health status classification system. Health Utilities Index Mark 2. Med Care 1996, 34:702-722.
• [5]Feeny D, Furlong W, Torrance GW, Goldsmith CH, Zhu Z, DePauw S, Denton M, Boyle M: Multiattribute and single-attribute utility functions for the health utilities index mark 3 system. Med Care 2002, 40:113-128.
• [6]Patrick DL, Bush JW, Chen MM: Methods for measuring levels of well-being for a health status index. Health Serv Res 1973, 8:228-245.
• [7]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.
• [8]Norman R, Cronin P, Viney R, King M, Street D, Ratcliffe J: International comparisons in valuing EQ-5D health states: a review and analysis. Value Health 2009, 12:1194-1200.
• [9]Jo MW, Yun SC, Lee SI: Estimating quality weights for EQ-5D health states with the time trade-off method in South Korea. Value Health 2008, 11:1186-1189.
• [10]Lee YK, Nam HS, Chuang LH, Kim KY, Yang HK, Kwon IS, Kind P, Kweon SS, Kim YT: South Korean time trade-off values for EQ-5D health states: modeling with observed values for 101 health states. Value Health 2009, 12:1187-1193.
• [11]Han C, Lee E, Iwaya T, Kataoka H, Kohzuki M: Development of the Korean version of Short-Form 36-Item Health Survey: health related QOL of healthy elderly people and elderly patients in Korea. Tohoku J Exp Med 2004, 203(3):189-194.
• [12]Kim SH, Jo M-W, Lee S: Psychometric properties of the Korean short form-36 health survey version 2 for assessing the general population. Asian Nurs Res 2013, 7:61-6.
• [13]Brazier JE, Fukuhara S, Roberts J, Kharroubi S, Yamamoto Y, Ikeda S, Doherty J, Kurokawa K: Estimating a preference-based index from the Japanese SF-36. J Clin Epidemiol 2009, 62:1323-1331.
• [14]Chuang LH, Kind P: Converting the SF-12 into the EQ-5D: an empirical comparison of methodologies. Pharmacoeconomics 2009, 27:491-505.
• [15]Ara R, Brazier J: Deriving an algorithm to convert the eight mean SF-36 dimension scores into a mean EQ-5D preference-based score from published studies (where patient level data are not available). Value Health 2008, 11:1131-1143.
• [16]Mortimer D, Segal L: Comparing the incomparable? A systematic review of competing techniques for converting descriptive measures of health status into QALY-weights. Med Decis Making 2008, 28:66-89.
• [17]Li L, Fu AZ: Some methodological issues with the analysis of preference-based EQ-5D index score. Health Serv Outcomes Res Methodol 2009, 9:162-176.
• [18]Rowen D, Brazier J, Roberts J: Mapping SF-36 onto the EQ-5D index: how reliable is the relationship? Health Qual Life Outcomes 2009, 7:27. BioMed Central Full Text
• [19]Sullivan PW, Ghushchyan V: Mapping the EQ-5D index from the SF-12: US general population preferences in a nationally representative sample. Med Decis Making 2006, 26:401-409.
• [20]Le QA, Doctor JN: Probabilistic mapping of descriptive health status responses onto health state utilities using Bayesian networks: an empirical analysis converting SF-12 into EQ-5D utility index in a national US sample. Med Care 2011, 49:451-460.
• [21]Kim TH, Jo MW, Lee SI, Kim SH, Chung SM: Psychometric properties of the EQ-5D-5L in the general population of South Korea. Qual Life Res 2013, 22(8):2245-53.
• [22]Lee WJ, Song KH, Noh JH, Choi YJ, Jo MW: Health-related quality of life using the EuroQol 5D questionnaire in Korean patients with type 2 diabetes. J Korean Med Sci 2012, 27:255-260.
• [23]Kim SG, Jo MW, Kim SH, Son WS, Lee JY, Lee SI: Factors affecting health-related quality of life in persons with stroke using SF-6D. Korean J Health Promot 2011, 11:72-81.
• [24]Kim SH, Hwang JS, Kim TW, Hong YS, Jo MW: Validity and reliability of the EQ-5D for cancer patients in Korea. Support Care Cancer 2012, 20:3155-3160.
• [25]EuroQol G: EuroQol¿a new facility for the measurement of health-related quality of life. Health Policy 1990, 16:199-208.
• [26]Ware JE, Kosinski M, Bjorner JB, Turner-Bowker DM, Gandek B, Maruish ME: User¿s Manual for the SF-36v2® Health Survey. In Concepts, Measures, and Application. 2nd edition. Quality Metric Incorporated, Lincoln, RI; 2007:13-24.
• [27]Brazier JE, Yang Y, Tsuchiya A, Rowen DL: A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. Eur J Health Econ 2010, 11(2):215-225.
• [28]Longworth L, Rowen D: The use of mapping methods to estimate health state utility values. 2011.
• [29]Dow WH, Norton EC: Choosing between and interpreting the Heckit and two-part models for corner solutions. Health Serv Outcomes Res Methodol 2003, 4:5-18.
• [30]Gray AM, Rivero-Arias O, Clarke PM: Estimating the association between SF-12 responses and EQ-5D utility values by response mapping. Med Decis Making 2006, 26:18-29.
• [31]Walters SJ, Brazier JE: Comparison of the minimally important difference for two health state utility measures: EQ-5D and SF-6D. Qual Life Res 2005, 14:1523-1532.
• [32]Chan KKW, Willan AR, Gupta M, Pullenayegum E: Underestimation of uncertainties in health utilities derived from mapping algorithms involving health-related quality-of-life measures: statistical explanations and potential remedies.Med Decis Making 2014, ?:?. doi:10.1177/0272989X13517750.