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Commentary. In Praise of Studies That Use More Than One Generic Preference-Based Measure

Published online by Cambridge University Press:  12 July 2019

David Feeny ,
William Furlong  and
George W. Torrance
David Feeny*
Affiliation:
Department of Economics and Centre for Health Economics and Policy Analysis, McMaster University, Hamilton, ONCanada; Health Utilities Incorporated, Dundas ONCanada
William Furlong
Affiliation:
Centre for Health Economics and Policy Analysis, McMaster University, Hamilton, ONCanada; Health Utilities Incorporated, Dundas ONCanada
George W. Torrance
Affiliation:
Centre for Health Economics and Policy Analysis, McMaster University, Hamilton, ONCanada; Health Utilities Incorporated, Dundas ONCanada
*
Author for correspondence: David Feeny, E-mail: [email protected]
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Abstract

Objectives and Background

Generic preference-based (GPB) measures of health-related quality of life (HRQL) are widely used as outcome measures in cost-effectiveness and cost-utility analyses (CEA, CUA). Health technology assessment agencies favor GPB measures because they facilitate comparisons among conditions and because the scoring functions for these measures are based on community preferences. However, there is no gold standard HRQL measure, scores generated by GPB measures may differ importantly, and changes in scores may fail to detect important changes in HRQL. Therefore, to enhance the accumulation of empirical evidence on how well GPB measures perform, we advocate that investigators routinely use two (or more) GPB measures in each study.

Methods

We discuss key measurement properties and present examples to illustrate differences in responsiveness for several major GPB measures across a wide variety of health contexts. We highlight the contributions of longitudinal head-to-head studies.

Results

There is substantial evidence that the performance of GPB measures varies importantly among diseases and health conditions. Scores are often not interchangeable. There are numerous examples of studies in which one GPB measure was responsive while another was not.

Conclusions

Investigators should use two (or more) GPB measures. Study protocols should designate one measure as the primary outcome measure; the other measure(s) would be used in secondary analyses. As evidence accumulates it will better inform the relative strengths and weaknesses of alternative GPB measures in various clinical conditions. This will facilitate the selection and interpretation of GPB measures in future studies.

Keywords

Comparative effectiveness researchCost-effectiveness analysisCost-utility analysisEuroQol EQ-5DGeneric preference-based measuresHealth-related quality of lifeHealth technology assessmentHealth Utilities IndexMulti-attribute utility measuresOutcome measuresQuality-adjusted life-yearsQuality of Well Being ScaleShort-Form 6D
Type
Article Commentary
Information
International Journal of Technology Assessment in Health Care , Volume 35 , Issue 4 , 2019 , pp. 257 - 262
Copyright
Copyright © Cambridge University Press 2019 

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Footnotes

Ethics approval and consent to participate: Not applicable. Availability of data and materials: Not applicable. Competing interests: It should be noted that all three authors have a proprietary interest in Health Utilities Incorporated, Dundas, Ontario, Canada. HUInc. distributes copyrighted Health Utilities Index (HUI) materials and provides methodological advice on the use of the HUI. Funding: There was no funding source for this study. Authors’ contributions: All three authors contributed to the conceptualization and writing of the study and have approved the final version of the manuscript. The authors acknowledge the constructive comments by the Editor, Deputy Editor, Associate Editor, and two reviewers, which have improved the study.

References

1.Feeny, D, Wu, L, Eng, K (2004) Comparing short form 6D, standard gamble, and Health Utilities Index Mark 2 and Mark 3 utility scores: Results from total hip arthroplasty patients. Qual Life Res 13, 1659–1670.10.1007/s11136-004-6189-2Google Scholar
2.Santana, MJ, Feeny, D, Johnson, JA, et al. (2010) Assessing the use of health-related quality-of-life measures in the routine care of lung-transplant patients. Qual Life Res 19, 371–379.10.1007/s11136-010-9599-3Google Scholar
3.Fryback, D, Dunham, NC, Palta, M, et al. (2007) U.S. norms for six generic health-related quality of life indexes from the National Health Measurement Study. Med Care 45, 1162–1170.10.1097/MLR.0b013e31814848f1Google Scholar
4.Guide to the methods of technology appraisal 2013. Process and methods [PMG9]. National Institute for Health and Care Excellence; April, 2013. https://www.nice.org.uk/process/pmg9/ (accessed January 17, 2019).Google Scholar
5.Guidelines for the economic evaluation of health technologies: Canada (4th ed). Canadian Agency for Drugs and Technologies in Health; March, 2017. https://www.cadth.ca/dv/guidelines-economic-evaluation-health-technologies-canada-4th-edition (accessed January 17, 2019).Google Scholar
6.The Pharmaceutical Benefits Advisory Committee guidelines: version 5.0. Australian Government Department of Health; September, 2016. https://pbac.pbs.gov.au/.Google Scholar
7.Henry, D (1992) Economic analysis as an aid to subsidisation decisions: The development of Australian guidelines for pharmaceuticals [review]. Pharmacoeconomics 1, 54–67.10.2165/00019053-199201010-00010Google Scholar
8.Shiroiwa, T, Fukuda, T, Ikeda, S, Takura, T, Moriwaki, K (2017) Development of an official guideline for economic evaluation of drugs/medical devices in Japan. Value Health 20, 372–378.10.1016/j.jval.2016.08.726Google Scholar
9.Neumann, PJ, Sanders, GD, Russell, LB, Siegel, JE, Ganiats, TG, eds (2017) Cost-effectiveness in health and medicine. 2nd ed. New York: Oxford University Press.Google Scholar
10.Feeny, D, Krahn, M, Prosser, LA, Salomon, JA (2017) Valuing health outcomes. In: Neumann, PJ, Sanders, GD, Russell, LB, Siegel, JE, Ganiats, TG, eds. Cost-effectiveness in health and medicine. 2nd ed. New York: Oxford University Press; p. 167–199.Google Scholar
11.Rabin, R, de Charro, F (2001) EQ-5D: A measure of health status from the EuroQol Group. Ann Med 33, 337–343.10.3109/07853890109002087Google Scholar
12.Torrance, GW, Feeny, DH, Furlong, WJ, et al. (1996) Multi-attribute preference functions for a comprehensive health status classification system: Health Utilities Index Mark 2. Med Care 34, 702–722.10.1097/00005650-199607000-00004Google Scholar
13.Furlong, W, Feeny, DH, Torrance, GW, Barr, RD (2001) The Health Utilities Index (HUI) system for assessing health-related quality of life in clinical studies. Ann Med 33, 375–384.10.3109/07853890109002092Google Scholar
14.Kaplan, RM, Anderson, JP (1996) The general health policy model: An integrated approach. In: Spilker, B, ed. Quality of life and pharmacoeconomics in clinical trials. 2nd ed. Philadelphia: Lippincott-Raven Press; p. 309–322.Google Scholar
15.Brazier, J, Roberts, J, Deverill, M (2002) The estimation of a preference-based measure of health status from the SF-36. J Health Econ 21, 271–292.10.1016/S0167-6296(01)00130-8Google Scholar
16.Brazier, JE, Roberts, J (2004) The estimation of a preference-based measure of health from the SF-12. Med Care 42, 851–859.10.1097/01.mlr.0000135827.18610.0dGoogle Scholar
17.Gamst-Klaussen, T, Chen, G, Lamu, AN, Olsen, JA (2016) Health state utility instruments compared: Inquiring into nonlinearity across EQ-5D-5L, SF-6D, HUI-3 and 15D. Qual Life Res 25, 1667–1678.10.1007/s11136-015-1212-3Google Scholar
18.Chen, G, Khan, MA, Iezzi, A, Ratcliffe, J, Richardson, J (2016) Mapping between 6 multiattribute utility instruments. Med Decis Making 36, 160–175.10.1177/0272989X15578127Google Scholar
19.Fayers, PM, Machin, D (2016) Quality of life: Assessment, analysis and reporting of patient-reported outcomes. 3rd ed. Chichester: John Wiley & Sons.Google Scholar
20.Feeny, DH, Eckstrom, E, Whitlock, EP, Perdue, LA (2013) A primer for systematic reviewers on the measurement of functional status and health-related quality of life in older adults. (Prepared by the Kaiser Permanente Research Affiliates Evidence-based Practice Center under Contract No. 290-2007-10057-I.) AHRQ Publication No. 13-EHC128-EF. Rockville, MD: Agency for Healthcare Research and Quality. September 2013. https://effectivehealthcare.ahrq6.gov/topics/quality-of-life-functional-status-measurement/white-paper (accessed January 19, 2019).Google Scholar
21.Guyatt, GH, Osoba, D, Wu, AW, Wyrwich, K, Norman, GR; Clinical Significance Consensus Meeting Group (2002) Methods to explain the clinical significance of health status measures. Mayo Clin Proc 77, 371–383.10.4065/77.4.371Google Scholar
22.Schünemann, HJ, Guyatt, GH (2005) Commentary - Goodbye M(C)ID! Hello MID, where do you come from? Health Serv Res 40, 593–597.10.1111/j.1475-6773.2005.0k375.xGoogle Scholar
23.Marra, CA, Woolcott, JC, Kopec, JA, et al. (2005) A comparison of generic, indirect utility measures (the HUI2, HUI3, SF-6D, and the EQ-5D) and disease-specific instruments (the RAQoL and the HAQ) in rheumatoid arthritis. Soc Sci Med 60, 1571–1582.10.1016/j.socscimed.2004.08.034Google Scholar
24.Marra, CA, Rashidi, AR, Guy, D, et al. (2005) Are indirect utility measures reliable and responsive in rheumatoid arthritis patients? Qual Life Res 14, 1333–1344.10.1007/s11136-004-6012-0Google Scholar
25.Fryback, DG, Palta, M, Cherepanov, D, Bolt, D, Kim, J-S (2010) Comparison of five health-related quality-of-life indexes using item response theory analysis. Med Decis Making 30, 5–15.10.1177/0272989X09347016Google Scholar
26.Lynch, FL, Dickerson, JF, Feeny, DH, Clarke, GN, MacMillan, AL (2016) Measuring health-related quality of life in teens with and without depression.” Med Care 54, 1089–1097.10.1097/MLR.0000000000000581Google Scholar
27.Dickerson, JF, Feeny, DH, Clarke, GN, MacMillan, AL, Lynch, FL (2018) Evidence on the longitudinal construct validity of major generic and utility measures of health-related quality of life in teens with depression. Qual Life Res 27, 447–454.10.1007/s11136-017-1728-9Google Scholar
28.Langfitt, JT, Vickery, BG, McDermott, MP, et al. (2006) Validity and responsiveness of generic preference-based HRQOL instruments in chronic epilepsy. Qual Life Res 15, 899–914.10.1007/s11136-005-5231-3Google Scholar
29.Pickard, AS, Johnson, JA, Feeny, DH (2005) Responsiveness of generic health-related quality of life measures in stroke. Qual Life Res 14, 207–219.10.1007/s11136-004-3928-3Google Scholar
30.Feeny, D, Spritzer, K, Hays, RD, et al. (2012) Agreement about identifying patients who change over time: Cautionary results in cataract and heart failure patients. Med Decis Making 32, 273–286.10.1177/0272989X11418671Google Scholar
31.Feeny, D (2013) Standardization and regulatory guidelines may inhibit science and reduce the usefulness of analyses based on the application of preference-based measures for policy decisions. Med Decis Making 33, 316–319.Google Scholar
32.Wiebe, S, Guyatt, G, Weaver, B, Matijevic, S, Sidwell, C (2003) Comparative responsiveness of generic and specific quality-of-life instruments. J Clin Epidemiol 56, 52–60.10.1016/S0895-4356(02)00537-1Google Scholar
33.Yang, Y, Brazier, J, Tsuchiya, A (2014) Effect of adding a sleep dimension to the EQ-5D descriptive system: A ‘bolt-on’ experiment. Med Decis Making 34, 42–53.10.1177/0272989X13480428Google Scholar
34.Drummond, MF, Sculpher, MJ, Torrance, GW, O'Brien, B, Stoddart, GL (2005) Methods for the economic evaluation of health care programmes. 3rd ed. Oxford: Oxford University Press.Google Scholar
35.Gold, MR, Patrick, DL, Torrance, GW, et al. (1996) Identifying and valuing outcomes. In: Gold, MR, Siegel, JE, Russell, LB, Weinstein, MC, eds. Cost-effectiveness in health and medicine. New York: Oxford University Press.Google Scholar
36.Richardson, J, Khan, MA, Iezzi, A, Maxwell, A (2015) Comparing and explaining differences in the magnitude, content, and sensitivity of utilities predicted by the EQ-5D, SF-6D, HUI3, 15D, QWB, and AQoL-8D multiattribute utility instruments. Med Decis Making 35, 276–291.10.1177/0272989X14543107Google Scholar
37.Richardson, J, McKie, J, Bariola, E (2014) Multiattribute Utility Instruments and their use. In: Culyer, AJ, ed., Encyclopedia of health economics. vol 2. San Diego: Elsevier; pp. 341–357.10.1016/B978-0-12-375678-7.00505-8Google Scholar
38.Brazier, J, Ara, R, Rowen, Donna, Chevrou-Severac, H (2017) A review of generic preference-based measures for use in cost-effectiveness models. Pharmacoeconomics 35, S21–S31.10.1007/s40273-017-0545-xGoogle Scholar
39.Devlin, N, Shah, KS, Feng, Y et al. (2018) Valuing health-related quality of life: An EQ-5D-3L value set for England. Health Econ 27, 7–22.10.1002/hec.3564Google Scholar
40.Dewitt, B, Feeny, D, Cella, D, et al. (2018) Estimation of a single preference-based summary score for the patient reported outcomes measurement information system: The PROMIS-Preference (PROPr) Score. Med Decis Making 38, 683–698.Google Scholar
41.Sintonen, H (2001) The 15D instrument of health-related quality of life: Properties and applications. Ann Med 33, 328–336.10.3109/07853890109002086Google Scholar