Valuing child health utility 9D health states with a young adolescent sample: A feasibility study to compare best-worst scaling discrete-choice experiment, standard gamble and time trade-off methods

Applied Health Economics and Health Policy

Volumn 9, Issue 1, 2011, Pages 15-27

  Ratcliffe, Julie ^a   Couzner, Leah ^a   Flynn, Terry ^b   Sawyer, Michael ^c   Stevens, Katherine ^d   Brazier, John ^d   Burgess, Leonie ^b  

^a FLINDERS UNIVERSITY   (Australia)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^c UNIVERSITY OF ADELAIDE   (Australia)

^d UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Adolescents; Utility measurement.

Indexed keywords

ADOLESCENT; ARTICLE; CHILD; CHILD HEALTH UTILITY 9D; COMPREHENSION; CONTROLLED STUDY; DECISION MAKING; FEASIBILITY STUDY; FEMALE; HEALTH ASSESSMENT QUESTIONNAIRE; HEALTH STATUS; HUMAN; HUMAN EXPERIMENT; INTERMETHOD COMPARISON; INTERVIEW; MALE; NORMAL HUMAN; QUALITY ADJUSTED LIFE YEAR; RATING SCALE; SCHOOL CHILD;

ADOLESCENT; COST-BENEFIT ANALYSIS; FEASIBILITY STUDIES; FEMALE; HEALTH STATUS; HUMANS; INTERVIEWS AS TOPIC; MALE; QUALITY OF LIFE; QUALITY-ADJUSTED LIFE YEARS; REPRODUCIBILITY OF RESULTS;

EID: 78650639940     PISSN: 11755652     EISSN: 11791896     Source Type: Journal    
DOI: 10.2165/11536960-000000000-00000     Document Type: Article

References (38)

1. Treatment adherence in adolescents: development and psychopathology. Clin Child Psychol Psychiatry 2001, 6:137-50
2. Kovacs M, Goldston D, Obrosky DS, et al. Prevalence and predictors of pervasive noncompliance with medical treatment among youths with insulin-dependent diabetes mellitus. J Am Acad Child Adolesc Psychiatry 1992 Nov; 31 (6): 1112-9
3. Dolgin M, Katz E, Doctors S, et al. Caregivers perception of medical compliance in adolescents with cancer. J Adolesc Health Care 1986; 7: 22-7
4. Berg J, Dischler J, Wagner D, et al. Medication compliance: a health care problem. Ann Pharmacotherapy 1993; 27 Suppl.: 2-21
5. DrummondMF, SculpherMJ, TorranceGW, et al. Methods for the economic evaluation of health care programmes. 3rd ed. Oxford: Oxford University Press, 2005
6. Brazier J, Ratcliffe J, Salomon J, et al. Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press, 2007
7. Ungar W, Santos M. Trends in paediatric health economic evaluation 1980-1999. Arch Dis Child 2004; 89: 26-9
8. Griebsch I, Coast J, Brown J. Quality adjusted life years lack quality in paediatric care: a critical review of published studies. Paediatrics 2005; 115: 600-14
9. Gunning JP. Understanding democracy: an introduction to public choice. Taiwan: Nomad Press, 2003
10. Tylee A, Haller D, Graham T, et al. Youth friendly primary care services: how are we doing and what more needs to be done. Lancet 2007; 369: 1585-73
11. Cavet J, Sloper P. The participation of children and young people in decisions about UK service development. Child Care Health Dev 2004; 30: 613-21
12. Saigal S, Stoskopf B, Feeny D, et al. Differences in preferences for neonatal outcomes among health care professionals, parents and adolescents. JAMA 1999; 281: 1991-7
13. Wasserman J, Aday L, Begley C, et al. Measuring health state preferences for hemophilia: development of a diseasespecific instrument. Haemophilia 2005; 11: 49-57
14. Norquist G, McGuire T, Essock S. Cost effectiveness of depression treatment for adolescents. Am J Psychiatry 2008; 165: 549-52
15. Varni JW, Seid M, Kurtin PS. PedsQL 4.0: reliability and validity of the Pediatric Quality of Life Inventory version 4.0 generic core scales in healthy and patient populations. Med Care 2001; 39 (80): 800-12
16. Ravens-Sieberer U, Gosch A, Rajmil L. The KIDSCREEN- 52 quality of life measure for children and adolescents: psychometric results from a cross cultural survey in 13 European countries. Value Health 2008; 11: 645-58
17. Stevens K. Developing a preference-based measure of health in children [PhD thesis]. Sheffield: ScHARR, University of Sheffield, 2008
18. Torrance G, Feeny D, Furlong W, et al. Multiattribute utility function for a comprehensive health status classification system: Health Utilities Index Mark 2. Med Care 1996; 34: 702-22
19. Stevens K. Working with children to develop dimensions for a preference-based generic, paediatric, health-related quality-of-life measure. Qual Health Res 2010; 20 (3): 340-51
20. Stevens K. Developing a descriptive system for a new preference- based measure of health related quality of life for children. Qual Life Res 2009; 18: 1105-13
21. McCabe C, Brazier J, Gilks P, et al. Using rank data to estimate health state utility models. J Health Econ 2006; 25 (3): 418-31
22. Ratcliffe J, Brazier J, Tsuchiya A, et al. Using DCE and ranking data to estimate cardinal values for health states for deriving a preference-based single index from the sexual quality of life questionnaire. Health Econ 2009; 18 (11): 1261-76
23. Salomon JA. Reconsidering the use of rankings in the valuation of health states: a model for estimating cardinal values from ordinal data. Popul Health Metr 2003 Dec 19; 1 (1): 12
24. Craig B, Busschbach J, Salomon J. Keep it simple: ranking health states yields values similar to cardinal measurement approaches. J Clin Epidemiol 2009; 62: 296-305
25. Ratcliffe J, Buxton M. Patients preferences regarding the process and outcomes of high technology medicine: an application of conjoint analysis to liver transplantation. Int J Technol Assess Health Care 1999; 15: 340-51
26. Coast J, Flynn T, Natarajan L, et al. Valuing the ICECAP capability index for older people. Soc Sci Med 2008: 67; 874-992
27. Ryan M, Netten A, Ska? tun D, et al. Using discrete choice experiments to estimate a preference-based measure of outcome: an application to social care for older people. J Health Econ 2006; 25 (5): 927-44
28. Hakim Z, Pathak D. Modelling the EuroQol data: a comparison of discrete choice conjoint and conditional preference modelling. Health Econ 1999; 82: 103-16
29. Louviere J, Hensher D, Swait J. Stated choice methods: analysis and application. Cambridge: University of Cambridge, 2000
30. Finn A, Louviere J. Determining the appropriate response to evidence of public concern: the case of food safety. J Public Pol Mark 1992; 11: 12-25
31. McIntosh W, Louviere J. Separating weight and scale value: an exploration of best-attribute scaling in health economics. Health Economists Study Group Meeting; Uxbridge; 2002 Jul
32. Marley AAJ, Flynn TN, Louviere JJ. Probabilistic models of set-dependent and attribute-level best-worst choice. J Math Psychol 2008; 52: 281-96
33. Flynn TN, Louviere JJ, Peters TJ, et al. Best-worst scaling: what it can do for health care research and how to do it. J Health Econ 2007; 26 (1): 171-37
34. Flynn TN, Louviere JJ, Peters TJ, et al. Estimating preferences for a dermatology consultation using best-worst scaling: comparison of various methods of analysis. BMC Med Res Methodol 2008; 8: 76
35. Myschool [online]. Available from URL: http://www. myschool.com.au/ [Accessed 2010 Sep 28]
36. Fiebig D, Keane M, Louviere J, et al. The generalized multinomial logit model: accounting for scale and coefficient heterogeneity. Mark Sci 2010; 29 (3): 393-421
37. Flynn TN, Louviere JJ, Marley AA, et al. Rescaling quality of life values from discrete choice experiments for use as QALYs: a cautionary tale. Popul Health Metr 2008 Oct 22; 6: 6
38. Street DJ, Burgess L. The construction of optimal stated choice experiments: theory and methods. Hoboken (NJ): Wiley, 2007