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The European Journal of Health Economics

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Open Access 01-11-2019 | Original Paper

Mapping the kidney disease quality of life 36-item short form survey (KDQOL-36) to the EQ-5D-3L and the EQ-5D-5L in patients undergoing dialysis

Authors: Fan Yang, Carlos K. H. Wong, Nan Luo, James Piercy, Rebecca Moon, James Jackson

Published in: The European Journal of Health Economics | Issue 8/2019

Abstract

Objectives

To develop algorithms mapping the Kidney Disease Quality of Life 36-Item Short Form Survey (KDQOL-36) onto the 3-level EQ-5D questionnaire (EQ-5D-3L) and the 5-level EQ-5D questionnaire (EQ-5D-5L) for patients with end-stage renal disease requiring dialysis.

Methods

We used data from a cross-sectional study in Europe (France, n = 299; Germany, n = 413; Italy, n = 278; Spain, n = 225) to map onto EQ-5D-3L and data from a cross-sectional study in Singapore (n = 163) to map onto EQ-5D-5L. Direct mapping using linear regression, mixture beta regression and adjusted limited dependent variable mixture models (ALDVMMs) and response mapping using seemingly unrelated ordered probit models were performed. The KDQOL-36 subscale scores, i.e., physical component summary (PCS), mental component summary (MCS), three disease-specific subscales or their average, i.e., kidney disease component summary (KDCS), and age and sex were included as the explanatory variables. Predictive performance was assessed by mean absolute error (MAE) and root mean square error (RMSE) using 10-fold cross-validation.

Results

Mixture models outperformed linear regression and response mapping. When mapping to EQ-5D-3L, the ALDVMM model was the best-performing one for France, Germany and Spain while beta regression was best for Italy. When mapping to EQ-5D-5L, the ALDVMM model also demonstrated the best predictive performance. Generally, models using KDQOL-36 subscale scores showed better fit than using the KDCS.

Conclusions

This study adds to the growing literature suggesting the better performance of the mixture models in modelling EQ-5D and produces algorithms to map the KDQOL-36 onto EQ-5D-3L (for France, Germany, Italy, and Spain) and EQ-5D-5L (for Singapore).

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Jha, V., Garcia-Garcia, G., Iseki, K., Li, Z., Naicker, S., Plattner, B., Saran, R., Wang, A.Y., Yang, C.W.: Chronic kidney disease: global dimension and perspectives. Lancet 382(9888), 260–272 (2013). https://doi.org/10.1016/S0140-6736(13)60687-XCrossRefPubMed

White, S.L., Chadban, S.J., Jan, S., Chapman, J.R., Cass, A.: How can we achieve global equity in provision of renal replacement therapy? Bull. World Health Organ. 86(3), 229–237 (2008)CrossRefPubMedPubMedCentral

Finkelstein, F.O., Arsenault, K.L., Taveras, A., Awuah, K., Finkelstein, S.H.: Assessing and improving the health-related quality of life of patients with ESRD. Nat Rev Nephrol 8(12), 718–724 (2012). https://doi.org/10.1038/nrneph.2012.238CrossRefPubMed

Peipert, J.D., Bentler, P.M., Klicko, K., Hays, R.D.: Psychometric properties of the kidney disease quality of life 36-item short-form survey (KDQOL-36) in the United States. Am. J. Kidney Dis. 71(4), 461–468 (2018). https://doi.org/10.1053/j.ajkd.2017.07.020CrossRefPubMed

Chen, J.Y., Choi, E.P., Wan, E.Y., Chan, A.K., Tsang, J.P., Chan, K.H., Lo, W.K., Lui, S.L., Chu, W.L., Lam, C.L.: Validation of the disease-specific components of the kidney disease quality of life-36 (KDQOL-36) in Chinese patients undergoing maintenance dialysis. PLoS One 11(5), e0155188 (2016). https://doi.org/10.1371/journal.pone.0155188CrossRefPubMedPubMedCentral

Yang, F., Wang, V.W., Joshi, V.D., Lau, T.W., Luo, N.: Validation of the english version of the kidney disease quality of life questionnaire (KDQOL-36) in haemodialysis patients in Singapore. Patient 6(2), 135–141 (2013). https://doi.org/10.1007/s40271-013-0015-2CrossRefPubMed

Register, F.: Medicare and medicaid programs; conditions for coverage for end-stage renal disease facilities; final rule. https://www.cms.gov/Regulations-and-Guidance/Legislation/CFCsAndCoPs/Downloads/ESRDfinalrule0415.pdf (2008). Accessed 3 Oct 2018

Hays, R.D., Kallich, J.D., Mapes, D.L., Coons, S.J., Carter, W.B.: Development of the kidney disease quality of life (KDQOL) instrument. Qual. Life Res. 3(5), 329–338 (1994)CrossRefPubMed

Rabin, R., de Charro, F.: EQ-5D: a measure of health status from the EuroQol Group. Ann. Med. 33(5), 337–343 (2001)CrossRefPubMed

10.

Wyld, M., Morton, R.L., Hayen, A., Howard, K., Webster, A.C.: A systematic review and meta-analysis of utility-based quality of life in chronic kidney disease treatments. PLoS Med. 9(9), e1001307 (2012). https://doi.org/10.1371/journal.pmed.1001307CrossRefPubMedPubMedCentral

11.

Dakin, H., Abel, L., Burns, R., Yang, Y.: Review and critical appraisal of studies mapping from quality of life or clinical measures to EQ-5D: an online database and application of the MAPS statement. Health Qual Life Outcomes 16(1), 31 (2018). https://doi.org/10.1186/s12955-018-0857-3CrossRefPubMedPubMedCentral

12.

Franks, P., Lubetkin, E.I., Gold, M.R., Tancredi, D.J.: Mapping the SF-12 to preference-based instruments: convergent validity in a low-income, minority population. Med. Care 41(11), 1277–1283 (2003). https://doi.org/10.1097/01.MLR.0000093480.58308.D8CrossRefPubMed

13.

Franks, P., Lubetkin, E.I., Gold, M.R., Tancredi, D.J., Jia, H.: Mapping the SF-12 to the EuroQol EQ-5D index in a national US sample. Med. Decis. Mak. 24(3), 247–254 (2004)CrossRef

14.

Lawrence, W.F., Fleishman, J.A.: Predicting EuroQoL EQ-5D preference scores from the SF-12 Health Survey in a nationally representative sample. Med. Decis. Mak. 24(2), 160–169 (2004). https://doi.org/10.1177/0272989X04264015CrossRef

15.

Gray, A.M., Rivero-Arias, O., Clarke, P.M.: Estimating the association between SF-12 responses and EQ-5D utility values by response mapping. Med. Decis. Mak. 26(1), 18–29 (2006). https://doi.org/10.1177/0272989X05284108CrossRef

16.

Yang, F., Devlin, N., Luo, N.: Impact of mapped EQ-5D utilities on cost-effectiveness analysis: in the case of dialysis treatments. Eur. J. Health Econ. (2018). https://doi.org/10.1007/s10198-018-0987-xCrossRefPubMedPubMedCentral

17.

Ware Jr., J., Kosinski, M., Keller, S.D.: A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Med. Care 34(3), 220–233 (1996)CrossRefPubMed

18.

Saban, K.L., Bryant, F.B., Reda, D.J., Stroupe, K.T., Hynes, D.M.: Measurement invariance of the kidney disease and quality of life instrument (KDQOL-SF) across veterans and non-veterans. Health Qual Life Outcomes 8, 120 (2010). https://doi.org/10.1186/1477-7525-8-120CrossRefPubMedPubMedCentral

19.

https://www.rand.org/health-care/surveys_tools/kdqol.html. Accessed 20 June 2019

20.

Rabin, R., de Charro, F.: EQ-5D: a measure of health status from the EuroQol group. Ann. Med. 33(5), 337–343 (2001). https://doi.org/10.3109/07853890109002087CrossRefPubMed

21.

Anderson, P., Benford, M., Harris, N., Karavali, M., Piercy, J.: Real-world physician and patient behaviour across countries: disease-specific programmes—a means to understand. Curr. Med. Res. Opin. 24(11), 3063–3072 (2008). https://doi.org/10.1185/03007990802457040CrossRefPubMed

22.

Eriksson, D., Goldsmith, D., Teitsson, S., Jackson, J., van Nooten, F.: Cross-sectional survey in CKD patients across Europe describing the association between quality of life and anaemia. BMC Nephrol 17(1), 97 (2016). https://doi.org/10.1186/s12882-016-0312-9CrossRefPubMedPubMedCentral

23.

Chevalier, J., de Pouvourville, G.: Valuing EQ-5D using time trade-off in France. Eur J Health Econ 14(1), 57–66 (2013). https://doi.org/10.1007/s10198-011-0351-xCrossRefPubMed

24.

Claes, C., Greiner, W., Uber, A., Graf von der Schulenburg, J.: An interview-based comparison of the TTO and VAS values given to EuroQol states of health by the general German population. In: Proceedings of the 15th Plenary Meeting of the EuroQol Group. Hannover, Germany: Centre for Health Economics and Health Systems Research, University of Hannover, pp. 13–38 (1999)

25.

Scalone, L., Cortesi, P.A., Ciampichini, R., Belisari, A., D’Angiolella, L.S., Cesana, G., Mantovani, L.G.: Italian population-based values of EQ-5D health states. Value Health 16(5), 814–822 (2013). https://doi.org/10.1016/j.jval.2013.04.008CrossRefPubMed

26.

Badia, X., Roset, M., Herdman, M., Kind, P.: A comparison of United Kingdom and Spanish general population time trade-off values for EQ-5D health states. Med. Decis. Mak. 21(1), 7–16 (2001). https://doi.org/10.1177/0272989X0102100102CrossRef

27.

Dolan, P.: Modeling valuations for EuroQol health states. Med. Care 35(11), 1095–1108 (1997)CrossRefPubMed

28.

Yang, F., Lau, T., Lee, E., Vathsala, A., Chia, K.S., Luo, N.: Comparison of the preference-based EQ-5D-5L and SF-6D in patients with end-stage renal disease (ESRD). Eur.J. Health Econ. 16(9), 1019–1026 (2015). https://doi.org/10.1007/s10198-014-0664-7CrossRefPubMed

29.

Round, J., Hawton, A.: Statistical alchemy: conceptual validity and mapping to generate health state utility values. Pharm. Open 1(4), 233–239 (2017). https://doi.org/10.1007/s41669-017-0027-2CrossRef

30.

Hinkle, D.E., Wiersma, W., Jurs, S.G.: Applied statistics for the behavioral sciences, vol. 663. Houghton Mifflin, Boston (2003)

31.

Brazier, J.E., Yang, Y., Tsuchiya, A., Rowen, D.L.: A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. Eur J Health Econ 11(2), 215–225 (2010). https://doi.org/10.1007/s10198-009-0168-zCrossRefPubMed

32.

Longworth, L., Yang, Y., Young, T., Mulhern, B., Hernandez Alava, M., Mukuria, C., Rowen, D., Tosh, J., Tsuchiya, A., Evans, P., Devianee Keetharuth, A., Brazier, J.: Use of generic and condition-specific measures of health-related quality of life in NICE decision-making: a systematic review, statistical modelling and survey. Health Technol. Assess. 18(9), 1–224 (2014). https://doi.org/10.3310/hta18090CrossRefPubMedPubMedCentral

33.

Basu, A., Manca, A.: Regression estimators for generic health-related quality of life and quality-adjusted life years. Med. Decis. Mak. 32(1), 56–69 (2012). https://doi.org/10.1177/0272989X11416988CrossRef

34.

Gray, L.A., Hernandez Alava, M., Wailoo, A.J.: Development of methods for the mapping of utilities using mixture models: mapping the AQLQ-S to the EQ-5D-5L and the HUI3 in patients with asthma. Value Health 21(6), 748–757 (2018). https://doi.org/10.1016/j.jval.2017.09.017CrossRefPubMedPubMedCentral

35.

Kent, S., Gray, A., Schlackow, I., Jenkinson, C., McIntosh, E.: Mapping from the Parkinson’s disease questionnaire PDQ-39 to the generic EuroQol EQ-5D-3L: the value of mixture models. Med. Decis. Mak. 35(7), 902–911 (2015). https://doi.org/10.1177/0272989X15584921CrossRef

36.

Khan, I., Morris, S., Pashayan, N., Matata, B., Bashir, Z., Maguirre, J.: Comparing the mapping between EQ-5D-5L, EQ-5D-3L and the EORTC-QLQ-C30 in non-small cell lung cancer patients. Health Qual Life Outcomes 14, 60 (2016). https://doi.org/10.1186/s12955-016-0455-1CrossRefPubMedPubMedCentral

37.

Khan, K.A., Madan, J., Petrou, S., Lamb, S.E.: Mapping between the Roland Morris Questionnaire and generic preference-based measures. Value Health 17(6), 686–695 (2014). https://doi.org/10.1016/j.jval.2014.07.001CrossRefPubMed

38.

Hernandez Alava, M., Wailoo, A.J., Ara, R.: Tails from the peak district: adjusted limited dependent variable mixture models of EQ-5D questionnaire health state utility values. Value Health 15(3), 550–561 (2012). https://doi.org/10.1016/j.jval.2011.12.014CrossRefPubMed

39.

Gray, L.A., Wailoo, A.J., Hernandez Alava, M.: Mapping the FACT-B instrument to EQ-5D-3L in patients with breast cancer using adjusted limited dependent variable mixture models versus response mapping. Value Health 21(12), 1399–1405 (2018). https://doi.org/10.1016/j.jval.2018.06.006CrossRefPubMedPubMedCentral

40.

Wailoo, A., Hernandez, M., Philips, C., Brophy, S., Siebert, S.: Modeling health state utility values in ankylosing spondylitis: comparisons of direct and indirect methods. Value Health 18(4), 425–431 (2015). https://doi.org/10.1016/j.jval.2015.02.016CrossRefPubMed

41.

Hernandez Alava, M., Wailoo, A., Wolfe, F., Michaud, K.: A comparison of direct and indirect methods for the estimation of health utilities from clinical outcomes. Med. Decis. Mak. 34(7), 919–930 (2014). https://doi.org/10.1177/0272989X13500720CrossRef

42.

van Hout, B., Janssen, M.F., Feng, Y.S., Kohlmann, T., Busschbach, J., Golicki, D., Lloyd, A., Scalone, L., Kind, P., Pickard, A.S.: Interim scoring for the EQ-5D-5L: mapping the EQ-5D-5L to EQ-5D-3L value sets. Value Health 15(5), 708–715 (2012). https://doi.org/10.1016/j.jval.2012.02.008CrossRefPubMed

43.

Gray, L.A., Alava, M.H.: A command for fitting mixture regression models for bounded dependent variables using the beta distribution. Sata J. 18(1), 51–75 (2018)CrossRef

44.

Wong, C.K., Lam, C.L., Rowen, D., McGhee, S.M., Ma, K.P., Law, W.L., Poon, J.T., Chan, P., Kwong, D.L., Tsang, J.: Mapping the functional assessment of cancer Therapy-general or -Colorectal to SF-6D in Chinese patients with colorectal neoplasm. Value Health 15(3), 495–503 (2012). https://doi.org/10.1016/j.jval.2011.12.009CrossRefPubMed

45.

Longworth, L., Rowen, D.: Mapping to obtain EQ-5D utility values for use in NICE health technology assessments. Value Health 16(1), 202–210 (2013). https://doi.org/10.1016/j.jval.2012.10.010CrossRefPubMed

46.

Ara, R., Rowen, D., Mukuria, C.: The use of mapping to estimate health state utility values. Pharmacoeconomics 35(Suppl 1), 57–66 (2017). https://doi.org/10.1007/s40273-017-0548-7CrossRefPubMed

47.

Coca Perraillon, M., Shih, Y.C., Thisted, R.A.: Predicting the EQ-5D-3L preference index from the SF-12 health survey in a national US Sample: a finite mixture approach. Med. Decis. Mak. 35(7), 888–901 (2015). https://doi.org/10.1177/0272989X15577362CrossRef

48.

Petrou, S., Rivero-Arias, O., Dakin, H., Longworth, L., Oppe, M., Froud, R., Gray, A.: preferred reporting items for studies mapping onto preference-based outcome measures: the MAPS statement. Pharmacoeconomics 33(10), 985–991 (2015). https://doi.org/10.1007/s40273-015-0319-2CrossRefPubMedPubMedCentral

49.

Wailoo, A.J., Hernandez-Alava, M., Manca, A., Mejia, A., Ray, J., Crawford, B., Botteman, M., Busschbach, J.: Mapping to estimate health-state utility from non-preference-based outcome measures: an ISPOR good practices for outcomes research task force report. Value Health 20(1), 18–27 (2017). https://doi.org/10.1016/j.jval.2016.11.006CrossRefPubMed

50.

Law, E.H., Pickard, A.S., Xie, F., Walton, S.M., Lee, T.A., Schwartz, A.: Parallel valuation: a direct comparison of EQ-5D-3L and EQ-5D-5L societal value Sets. Med. Decis. Mak. 38(8), 968–982 (2018). https://doi.org/10.1177/0272989X18802797CrossRef

Title: Mapping the kidney disease quality of life 36-item short form survey (KDQOL-36) to the EQ-5D-3L and the EQ-5D-5L in patients undergoing dialysis
Authors: Fan Yang
Carlos K. H. Wong
Nan Luo
James Piercy
Rebecca Moon
James Jackson
Publication date: 01-11-2019
Publisher: Springer Berlin Heidelberg
Published in: The European Journal of Health Economics / Issue 8/2019
Print ISSN: 1618-7598
Electronic ISSN: 1618-7601
DOI: https://doi.org/10.1007/s10198-019-01088-5

At a glance: The STEP trials

Springer Medicine

Mapping the kidney disease quality of life 36-item short form survey (KDQOL-36) to the EQ-5D-3L and the EQ-5D-5L in patients undergoing dialysis

Abstract

Objectives

Methods

Results

Conclusions

At a glance: The STEP trials

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

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