The simultaneous valuation of states from multiple instruments using ranking and VAS data: methods and preliminary results

Rowen, D and Brazier, J and Tsuchiya, A and Hernández, M and Ibbotson, R (2009): The simultaneous valuation of states from multiple instruments using ranking and VAS data: methods and preliminary results.

Abstract

Background: Previous methods of empirical mapping involve using regressions on patient or general population self-report data from datasets involving two or more instruments. This approach relies on overlap in the descriptive systems of the measures, but key dimensions may not be present in both measures. Furthermore this assumes it is appropriate to use different instruments on the same population, which may not be the case for all patient groups. The aim of the study described here is to develop a new method of mapping using general population preferences for hypothetical health states defined by the descriptive systems of different measures. This paper presents a description of the methods used in the study and reports on the results of the valuation study including details about the respondents, feasibility and quality (e.g. response rate, completion and consistency) and descriptive results on VAS and ranking data. The use of these results to estimate mapping functions between instruments will be presented in a companion paper. Methods: The study used interviewer administered versions of ranking and VAS techniques to value 13 health states defined by each of 6 instruments: EQ-5D (generic), SF-6D (generic), HUI2 (generic for children), AQL-5D (asthma specific), OPUS (social care specific), ICECAP (capabilities). Each interview involved 3 ranking and visual analogue scale (VAS) tasks with states from 3 different instruments where each task involves the simultaneous valuation of multiple instruments. The study includes 13 health and well-being states for each instrument (16 for EQ-5D) that reflect a range of health state values according to the published health state values for each instrument and each health state is valued approximately 75-100 times. Results: The sample consists of 499 members of the UK general population with a reasonable spread of background characteristics (response rate=55%). The study achieved a completion rate of 99% for all states included in the rank and rating tasks and 94.8% of respondents have complete VAS responses and 97.2% have complete rank responses. Interviewers reported that it is doubtful for 4.1% of respondents that they understood the tasks, and 29.3% of respondents stated that they found the tasks difficult. The results suggest important differences in the range of mean VAS and mean rank values per state across instruments, for example mean VAS values for the worst state vary across instruments from 0.075 to 0.324. Respondents are able to change the ordering of states between the rank and VAS tasks and 12.0% of respondents have one or more differences in their rank and VAS orderings for every task. Conclusions: This study has demonstrated the feasibility of simultaneously valuing health states from different preference-based instruments. The preliminary analysis of the results presented here provides the basis for a new method of mapping between measures based on general population preferences.

• Item Type: MPRA Paper
• Language: English
• Keywords: preference-based measures of health; quality of life; mapping; visual analogue scale; ranking
• Subjects: I - Health, Education, and Welfare > I3 - Welfare, Well-Being, and Poverty > I31 - General Welfare, Well-Being; I - Health, Education, and Welfare > I1 - Health > I19 - Other

References

Barton GR, Bankart J, Davis AC, Summerfield QA (2004). Comparing utility scores before and after hearing aid provision: results according to the EQ-5D, HUI3 and SF-6D. Applied Health Economics and Health Policy 3:103-5.

Brazier J, Czoski-Murray C, Roberts J, Brown M, Symonds T, Kelleher C (2008). Estimation of a preference-based index from a condition-specific measure: the King's Health Questionnaire. Medical Decision Making 28:113-26.

Brazier J, Greene C, McCabe C, Stevens K (2003). Use of visual analog scales in economic evaluation. Expert Review of Pharmacoeconomics Outcomes Research 3:293-302.

Brazier J, Roberts J (2004). The estimation of a preference-based measure of health from the SF-12. Medical Care 42:851-9.

Brazier J, Roberts J, Deverill M (2002). The estimation of a preference-based measure of health from the SF-36. Journal of Health Economics 21:271-92.

Brazier J, Roberts J, Tsuchiya A, Busschbach J (2004). A comparison of the EQ-5D and SF-6D across seven patient groups. Health Economics 13:873-84.

Brooks R (1996). Euroqol: the current state of play. Health Policy 37:54-72.

Coast J, Flynn T, Natarajan L, Sprotson K, Lewis J, Louviere JL, Peters TJ (2008). Valuing the ICECAP capability index for older people. Social Science and Medicine 67:874-82.

Dolan P (1997). Modeling valuations for EuroQol health states. Medical Care 35:1095-108.

Dowie J (2002). Decision validity should determine whether a generic or condition-specific HRQOL measure is used in health care decisions. Health Economics 11:1-8.

Drummond MF, Sculpher MJ, Torrance GW, O'Brien BJ, Stoddart GL (2005). Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press.

Espallargues M, Czoski-Murray C, Bansback N, Carlton J, Lewis G, Hughes L, Brand C, Brazier J (2005). The impact of age related macular degeneration on health status utility values. Investigative Ophthalmology and Visual Science 46:4016-23.

Feeny D, Furlong W, Torrance GW, Goldsmith CH, Zhu Z, DePauw S, Denton M, Boyle M (2002). Multi-attribute and single-attribute utility functions for the Health Utilities Index Mark 3 system. Medical Care 40:113-28.

Franks P, Lubetkin EI, Gold MR, Tancredi DJ, Haomiao J (2004). Mapping the SF-12 to the EuroQol EQ-5D Index in a national US sample. Medical Decision Making 24:247-54.

Gray AM, Rivero-Arias O, Clarke PM (2006). Estimating the association between SF-12 responses and EQ-5D utility values by response mapping. Medical Decision Making 26:18-29.

Grewal I, Lewis J, Flynn TN, Brown J, Bond J, Coast J (2006). Developing attributes for a generic quality of life measure for older people: preferences or capabilities? Social Science and Medicine 62:1891-1901.

Gudex C, Dolan P, Kind P, Thomas R, Williams A (1997). Valuing health states: Interviews with the general public. European Journal of Public Health 7:441-8.

Harper R, Brazier JE, Waterhouse JC, Walters SJ, Jones NMB, Howard P (1997). A comparison of outcome measures for patients with chronic obstructive pulmonary disease (COPD) in an outpatient setting. Thorax 52:879-87.

Juniper EF, Guyatt GH, Ferrie PJ, Griffith LE (1993). Measuring quality of life in asthma. American Review of Respiratory Disease 147: 832-8.

Kaplan RM, Anderson JP (1998). A general health policy model: Update and applications. Health Services Research 23:203-35.

Kobelt G, Kirchberger I, Malone-Lee J (1999). Quality of life aspects of the overactive bladder and the effect of treatment with tolterodine. British Journal of Urology 83:583–90.

Longworth L, Bryan S (2003). An empirical comparison of EQ-5D and SF-6D in liver transplant patients. Health Economics 12:1061-7.

McCabe C, Stevens K, Roberts J, Brazier J (2005). Health state values for the HUI2 descriptive system: Results from a UK survey. Health Economics 14:231-44.

McCabe C, Brazier J, Gilks P, Tsuchiya A, Roberts J, O'Hagan A, Stevens K (2006). Using rank data to estimate health state utility models. Journal of Health Economics 25:418-31.

The MVH Group. The Measurement and Valuation of Health; First report on the main survey, May 1994.

Nichol MB, Sengupta N, Globe DR (2001). Evaluating quality adjusted life years: estimation of the Health Utility Index (HUI2) from the SF-36. Medical Decision Making 21:105-12.

O’Brien BJ, Spath M, Blackhouse G, Severens JL, Brazier JE (2003). A view from the bridge: agreement between the SF-6D utility algorithm and the Health Utilities Index. Health Economics 12:975-82.

Revicki DA, Leidy NK, Brennan-Deimer F, Sorenson S, Togias A (1998). Integrating patients' preferences into health outcomes assessment: The multi-attribute asthma symptom utility index. Chest 114:998-1007.

Rowen D, Brazier J, Tsuchiya A, Hernandez M (2009). Mapping between preference-based measures of health via a common yardstick. Health Economics and Decision Science Discussion Paper, forthcoming.

Ryan M, Netten A, Skatun D, Smith P (2006). Using discrete choice experiments to estimate a preference-based measure of outcome - An application to social care for older people. Journal of Health Economics 25:927-44.

Salomon JA (2003). Reconsidering the use of rankings in the valuation of health states: a model for estimating cardinal values from ordinal data. Population Health Metrics 1(12).

Salomon JA (2007). Using ordinal data to estimate cardinal valuations. In: Measuring and Valuing Health Benefits for Economic Evaluation, Brazier J, Ratcliffe J, Salomon JA, Tsuchiya A (eds). Oxford: Oxford University Press.

Stevens K (2009). Working with children to develop dimensions for a preference based generic, paediatric, health related quality of life measure. Qualitative Health Research, forthcoming.

Torrance GW, Feeny DH, Furlong WJ (2001). Visual analogue scales: do they have a role in the measurement of preferences for health states? Medical Decision Making 21:329-34.

Torrance GW, Feeny DH, Furlong WJ, Barr RD, Zhang Y, Wang Q (1996). A multiattribute utility function for a comprehensive health status classification system: Health Utilities Mark 2. Medical Care 34:702-22.

Tsuchiya A, Brazier J, McColl E, Parkin D (2002). Deriving preference-based single indices from non-preference based condition-specific instruments: Converting AQLQ into EQ5D indices, Health Economics and Decision Science Discussion Paper.

Tsuchiya A, Brazier J, Roberts J (2006). Comparison of valuation methods used to generate the EQ-5D and SF-6D value sets. Journal of Health Economics 25:334-46.

Yang Y, Brazier J, Tsuchiya A, Coyne K (2008). Estimating a preference-based single index from the Overactive Bladder Questionnaire. Value in Health 12(1):159-66.

Yang Y, Tsuchiya A, Brazier J, Young T (2007). Estimating a preference-based single index from the Asthma Quality of Life Questionnaire (AQLQ). Health Economics and Decision Science Discussion Paper.

Young T, Yang Y, Brazier J, Tsuchiya A (2007). The use of Rasch analysis as a tool in the construction of a preference based measure: the case of AQLQ. Health Economics and Decision Science Discussion Paper.

Ware JE, Snow KK, Kolinski M, Gandeck B (1993). SF-36 Health survey manual and interpretation guide. The Health Institute, New England Medical Centre, Boston, MA.