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Health and Quality of Life Outcomes

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Open Access 01-12-2020 | Research

Predicting EQ-5D-5L crosswalk from the PROMIS-29 profile for the United Kingdom, France, and Germany

Authors: Christoph Paul Klapproth, J. van Bebber, C. J. Sidey-Gibbons, J. M. Valderas, A. Leplege, M. Rose, F. Fischer

Published in: Health and Quality of Life Outcomes | Issue 1/2020

Abstract

Background

EQ-5D health state utilities (HSU) are commonly used in health economics to compute quality-adjusted life years (QALYs). The EQ-5D, which is country-specific, can be derived directly or by mapping from self-reported health-related quality of life (HRQoL) scales such as the PROMIS-29 profile. The PROMIS-29 from the Patient Reported Outcome Measures Information System is a comprehensive assessment of self-reported health with excellent psychometric properties. We sought to find optimal models predicting the EQ-5D-5L crosswalk from the PROMIS-29 in the United Kingdom, France, and Germany and compared the prediction performances with that of a US model.

Methods

We collected EQ-5D-5L and PROMIS-29 profiles and three samples representative of the general populations in the UK (n = 1509), France (n = 1501), and Germany (n = 1502). We used stepwise regression with backward selection to find the best models to predict the EQ-5D-5L crosswalk from all seven PROMIS-29 domains. We investigated the agreement between the observed and predicted EQ-5D-5L crosswalk in all three countries using various indices for the prediction performance, including Bland–Altman plots to examine the performance along the HSU continuum.

Results

The EQ-5D-5L crosswalk was best predicted in France (nRMSE_FRA = 0.075, nMAE_FRA = 0.052), followed by the UK (nRMSE_UK = 0.076, nMAE_UK = 0.053) and Germany (nRMSE_GER = 0.079, nMAE_GER = 0.051). The Bland–Altman plots show that the inclusion of higher-order effects reduced the overprediction of low HSU scores.

Conclusions

Our models provide a valid method to predict the EQ-5D-5L crosswalk from the PROMIS-29 for the UK, France, and Germany.

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Weinstein MC, Torrance G, McGuire A. QALYs: the basics. Value Heal (Internet). 2009;12(Suppl. 1):S5–9. https://doi.org/10.1111/j.1524-4733.2009.00515.x.

Klarman HE, Francis JO, Rosenthal GD. Cost effectiveness analysis applied to the treatment of chronic renal disease. Med Care (Internet). 1968;6(1):48–54. Available from: http://www.jstor.org/stable/3762651.

Valderas JM, Alonso J. Patient reported outcome measures : a model-based classification system for research and clinical practice. Qual Life Res. 2008;17:1125–35.CrossRef

Rabin R, Oemar M, Oppe M, Janssen B, Herdman M. EQ-5D-5L user guide version 2.1. 2015;(April):28. Available from: http://www.euroqol.org/fileadmin/user_upload/Documenten/PDF/Folders_Flyers/EQ-5D-5L_UserGuide_2015.pdf.

Greiner W, Weijnen T, Nieuwenhuizen MN, Oppe S, Badia X, et al. A single European currency for EQ-5D health states. Eur J Heal Econ. 2003;4:222–31.CrossRef

Herdman M, Gudex C, Lloyd A, Janssen M, Kind P, Parkin D. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual Life Res. 2011;20:1727–36.CrossRef

Devlin N, Krabbe P. The development of new research methods for the valuation of EQ-5D-5L. Eur J Heal Econ. 2013;14(Suppl.):1–3.

Fries JF, Cella D, Rose M, Krishnan E, Bruce B. Progress in assessing physical function in arthritis: PROMIS short forms and computerized adaptive testing. J Rheumatol. 2009;36(9):2061–6.CrossRef

Alonso J, Bartlett SJ, Rose M, Aaronson NK, Chaplin JE, Efficace F, et al. The case for an international patient-reported outcomes measurement information system (PROMIS^®) initiative. Health Qual Life Outcomes. 2013;11(1):1–5.CrossRef

10.

Embretson SE, Reise SP. Item response theory for psychologists. London: Psychology Press; 2013.CrossRef

11.

PROMIS Cooperative Group. PROMIS^® Instrument Maturity Model [Internet]. 2012. p. 1–4. Available from: http://www.healthmeasures.net/images/PROMIS/PROMISStandards_Vers_2_0_MaturityModelOnly_508.pdf.

12.

Rupp AA, Zumbo BD. Understanding parameter invariance in unidimensional IRT models. Educ Psychol Meas. 2006;66(1):63–84.CrossRef

13.

Fries JF, Witter J, Rose M, Cella D, Khanna D, Morgan-DeWitt E. Item response theory, computerized adaptive testing, and promis: assessment of physical function. J Rheumatol. 2014;41(1):153–8.CrossRef

14.

Hays RD, Revicki DA, Feeny D, Fayers P, Spritzer KL, Cella D. Using linear equating to map PROMIS global health items and the PROMIS-29 V2.0 profile measure to the health utilities index mark 3. Pharmacoeconomics. 34(10):1015–22.

15.

Terwee CB, Roorda LD, De Vet HCW, Dekker J, Westhovens R, Van Leeuwen J, et al. Dutch-Flemish translation of 17 item banks from the Patient-Reported Outcomes Measurement Information System (PROMIS). Qual Life Res. 2014;23(6):1733–41.PubMed

16.

Oude Voshaar MAH, ten Klooster PM, Taal E, Krishnan E, van de Laar MAFJ. Dutch translation and cross-cultural adaptation of the PROMIS^® physical function item bank and cognitive pre-test in Dutch arthritis patients. Arthritis Res Ther (Internet). 2012;14(2):R47. Available from: http://arthritis-research.com/content/14/2/R47.

17.

Mukuria C, Rowen D, Harnan S, Rawdin A, Wong R, Ara R, et al. An updated systematic review of studies mapping (or cross-walking) measures of health-related quality of life to generic preference-based measures to generate utility values. Appl Health Econ Health Policy (Internet). 2019;17(3):295–313. https://doi.org/10.1007/s40258-019-00467-6.CrossRef

18.

Dakin H. Review of studies mapping from quality of life or clinical measures to EQ-5D: an online database. Health Qual Life Outcomes (Internet). 2013;11(1):151. https://doi.org/10.1186/1477-7525-11-151.CrossRef

19.

Revicki DA, Kawata AK, Harnam N, Chen W-H, Hays RD, Cella D. 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. Qual Life Res. 2009;18(6):783–91.CrossRef

20.

Thompson NR, Lapin BR, Katzan IL. Mapping PROMIS global health items to EuroQol (EQ-5D) utility scores using linear and equipercentile equating. Pharmacoeconomics. 2017.

21.

Crott R. Direct mapping of the QLQ-C30 to EQ-5D preferences: a comparison of regression methods. PharmacoEconomics Open. 2018;2(2):165–77.CrossRef

22.

Hernández Alava M, Wailoo AJ, Ara R. Tails from the peak district: Adjusted limited dependent variable mixture models of EQ-5D questionnaire health state utility values. Value Heal (Internet). 2012;15(3):550–61. https://doi.org/10.1016/j.jval.2011.12.014.CrossRef

23.

Schalet BD, Cook KF, Choi SW, Cella D. Establishing a common metric for self-reported anxiety: linking the MASQ, PANAS, and GAD-7 to PROMIS anxiety. J Anxiety Disord (Internet). 2014;28(1):88–96.CrossRef

24.

Choi SW, Schalet B, Cook KF, Cella D. Establishing a common metric for depressive symptoms: linking the BDI-II, CES-D, and PHQ-9 to PROMIS depression. Psychol Assess. 2014;26(2):513–27.CrossRef

25.

Shaw JW, Johnson JA, Coons SJ. US valuation of the EQ-5D health states: development and testing of the D1 valuation model. Med Care. 2005;43(3):203–20.CrossRef

26.

Van Hout B, Janssen MF, Feng YS, Kohlmann T, Busschbach J, Golicki D, et al. Interim scoring for the EQ-5D-5L: Mapping the EQ-5D-5L to EQ-5D-3L value sets. Value Heal (Internet) 2012;15(5):708–15. https://doi.org/10.1016/j.jval.2012.02.008.

27.

Lamu AN, Chen G, Gamst-Klaussen T, Olsen JA. Do country-specific preference weights matter in the choice of mapping algorithms? The case of mapping the Diabetes-39 onto eight country-specific EQ-5D-5L value sets. Qual Life Res (Internet). 2018;27(7):1801–14. https://doi.org/10.1007/s11136-018-1840-5.

28.

Cella D, Choi SW, Condon DM, Schalet B, Hays RD, Rothrock NE, et al. PROMIS^® adult health profiles: efficient short-form measures of seven health domains. Value Heal. 2019;22(5):537–44.CrossRef

29.

Choi SW, Reise SP, Pilkonis PA, Hays RD, Cella D. Efficiency of static and computer adaptive short forms compared to full-length measures of depressive symptoms. Qual Life Res. 2010;19(1):125–36.CrossRef

30.

Hinchcliff M, Beaumont JL, Thavarajah K, Varga J, Chung A, Podlusky S, et al. Validity of two new patient-reported outcome measures in systemic sclerosis: patient-reported outcomes measurement information system 29-item health profile and functional assessment of chronic illness therapy-dyspnea short form. Arthritis Care Res (Hoboken) (Internet). 2011 Nov;63(11):1620–8. Available from: https://www.ncbi.nlm.nih.gov/pubmed/22034123.

31.

Beaumont JL, Cella D, Phan AT, Choi S, Liu Z, Yao JC. Comparison of health-related quality of life in patients with neuroendocrine tumors with quality of life in the general US population. Pancreas. 2012;41(3):461–6.CrossRef

32.

Yount SE, Beaumont JL, Chen S-Y, Kaiser K, Wortman K, Van Brunt DL, et al. Health-Related Quality of Life in Patients with Idiopathic Pulmonary Fibrosis. Lung (Internet). 2016 Apr;194(2):227–34. https://doi.org/10.1007/s00408-016-9850-y.

33.

Fischer F, Gibbons C, Coste J, Valderas JM, Rose M, Leplège A. Measurement invariance and general population reference values of the PROMIS Profile 29 in the UK, France, and Germany. Qual Life Res (Internet). 2018;27(4):999–1014. https://doi.org/10.1007/s11136-018-1785-8.

34.

Martí-Pastor M, Pont A, Ávila M, Garin O, Vilagut G, Forero CG, et al. Head-to-head comparison between the EQ-5D-5L and the EQ-5D-3L in general population health surveys. Popul Health Metr. 2018;16(1):1–11.CrossRef

35.

Janssen MF, Bonsel GJ, Luo N. Is EQ-5D-5L Better Than EQ-5D-3L? A Head-to-Head Comparison of Descriptive Systems and Value Sets from Seven Countries. Pharmacoeconomics (Internet). 2018;36(6):675–97. https://doi.org/10.1007/s40273-018-0623-8.

36.

Bernstein DN, Kelly M, Houck JR, Ketz JP, Flemister AS, DiGiovanni BF, et al. PROMIS pain interference is superior vs. numeric pain rating scale for pain assessment in foot and ankle patients. Foot Ankle Int. 2019;40(2):139–44.CrossRef

37.

Vrieze SI. Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychol Methods (Internet). 2012/02/06. 2012;17(2):228–43. Available from: https://www.ncbi.nlm.nih.gov/pubmed/22309957.

38.

Brazier JE, Yang Y, Tsuchiya A, Rownen DL. A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. Eur J Heal Econ. 2010;11:215–25.CrossRef

39.

Lamu AN. Does linear equating improve prediction in mapping? Crosswalking MacNew onto EQ-5D-5L value sets. Eur J Heal Econ [Internet]. 2020;21(6):903–15. https://doi.org/10.1007/s10198-020-01183-y.

40.

Lamu AN, Olsen JA. Testing alternative regression models to predict utilities: mapping the QLQ-C30 onto the EQ-5D-5L and the SF-6D. Qual Life Res (Internet). 2018;27(11):2823–39. https://doi.org/10.1007/s11136-018-1981-6.

41.

Gamst-Klaussen T, Lamu AN, Chen G, Olsen JA. Assessment of outcome measures for cost–utility analysis in depression: mapping depression scales onto the EQ-5D-5L. BJPsych Open. 2018;4(4):160–6.CrossRef

42.

Blum A, Kalai A, Langford J. Beating the holdout: bounds for KFold and progressive cross-validation. COLT. 1999;203–8.

43.

Collado-Mateo D, Chen G, Garcia-Gordillo MA, Iezzi A, Adsuar JC, Olivares PR, et al. Fibromyalgia and quality of life: mapping the revised fibromyalgia impact questionnaire to the preference-based instruments. Health Qual Life Outcomes. 2017;15(114):1–9.

44.

Marriott E-R, van Hazel G, Gibbs P, Hatswell AJ. Mapping EORTC-QLQ-C30 to EQ-5D-3L in patients with colorectal cancer. J Med Econ (Internet). 2017;20(2):193–9. https://doi.org/10.1080/13696998.2016.1241788.

45.

Ameri H, Yousefi M, Yaseri M, Nahvijou A, Arab M, Akbari Sari A. Mapping EORTC-QLQ-C30 and QLQ-CR29 onto EQ-5D-5L in Colorectal Cancer Patients. J Gastrointest Cancer. 2019 ([Epub ahead of print]).

46.

Beck AJCC, Kieffer JM, Retèl VP, van Overveld LFJ, Takes RP, van den Brekel MWM, et al. Mapping the EORTC QLQ-C30 and QLQ-H&N35 to the EQ-5D for head and neck cancer: can disease-specific utilities be obtained? PLoS ONE. 2019;14(12):1–16.CrossRef

47.

Yang F, Wong CKH, Luo N, Piercy J, Moon R, Jackson J. 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. Eur J Heal Econ [Internet]. 2019;20(8):1195–206. https://doi.org/10.1007/s10198-019-01088-5

48.

Pennington B, Davis S. Mapping from the health assessment questionnaire to the EQ-5D: the impact of different algorithms on cost-effectiveness results. Value Heal (Internet). 2014;17(8):762–71. https://doi.org/10.1016/j.jval.2014.11.002

49.

NICE. Guide to the methods of technology appraisal (internet). NICE Guidelines. 2013. Available from: nice.org.uk/process/pmg9.

50.

Ali FM, Kay R, Finlay AY, Piguet V, Kupfer J, Dalgard F, et al. Mapping of the DLQI scores to EQ-5D utility values using ordinal logistic regression. Qual Life Res. 2017;26(11):3025–34.CrossRef

51.

Hanmer J, Feeny D, Fischhoff B, Hays RD, Hess R, Pilkonis PA, et al. The PROMIS of QALYs. Health Qual Life Outcomes (Internet). 2015;15–7. https://doi.org/10.1186/s12955-015-0321-6.

52.

Hanmer J, Cella D, Feeny D, Fischhoff B, Hays RD, Hess R, et al. Selection of key health domains from PROMIS^® for a generic preference-based scoring system. Qual Life Res. 2017;2017:1–9.

53.

Hanmer J, Dewitt B. The development of a preference-based scoring system for PROMIS^® (PROPr): A Technical Report Version 1.4. 2017.

54.

Hanmer J, Cella D, Feeny D, Fischhoff B, Hays RD, Hess R, et al. Evaluation of options for presenting health-states from PROMIS^® item banks for valuation exercises. Qual Life Res (Internet). 2018;27(7):1835–43. https://doi.org/10.1007/s11136-018-1852-1.

55.

Dewitt B, Feeny D, Fischhoff B, Cella D, Hays RD, Hess R, et al. Estimation of a preference-based summary score for the patient-reported outcomes measurement information system: the PROMIS^®-Preference (PROPr) scoring system. Med Decis Mak. 2018;38(6):683–98.CrossRef

56.

Chevalier J, De Pouvourville G. Valuing EQ-5D using time trade-off in france. Eur J Heal Econ. 2013;14(1):57–66.CrossRef

57.

Hanmer J, Cella D, Feeny D, Fischhoff B, Hays RD, Hess R, et al. Selection of key health domains from PROMIS^® for a generic preference-based scoring system. Qual Life Res. 2017;26(12):3377–85.CrossRef

Title: Predicting EQ-5D-5L crosswalk from the PROMIS-29 profile for the United Kingdom, France, and Germany
Authors: Christoph Paul Klapproth
J. van Bebber
C. J. Sidey-Gibbons
J. M. Valderas
A. Leplege
M. Rose
F. Fischer
Publication date: 01-12-2020
Publisher: BioMed Central
Published in: Health and Quality of Life Outcomes / Issue 1/2020
Electronic ISSN: 1477-7525
DOI: https://doi.org/10.1186/s12955-020-01629-0

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Predicting EQ-5D-5L crosswalk from the PROMIS-29 profile for the United Kingdom, France, and Germany

Abstract

Background

Methods

Results

Conclusions

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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