Swipe om te navigeren naar een ander artikel
The aim of this study was to estimate preferences related to quality of life attributes in people with multiple sclerosis, by keeping heterogeneity of patient preference in mind, using the latent class approach.
A discrete choice experiment survey was developed using the following attributes: activities of daily living, instrumental activities of daily living, pain/fatigue, anxiety/depression and attention/concentration. Choice sets were presented as pairs of hypothetical health status, based upon a fractional factorial design.
The latent class logit model estimated on 152 patients identified three subpopulations, which, respectively, attached more importance to: (1) the physical dimension; (2) pain/fatigue and anxiety/depression; and (3) instrumental activities of daily living impairments, anxiety/depression and attention/concentration. A posterior analysis suggests that the latent class membership may be related to an individual’s age to some extent, or to diagnosis and treatment, while apart from energy dimension, no significant difference exists between latent groups, with regard to Multiple Sclerosis Quality of Life-54 scales.
A quality of life preference-based utility measure for people with multiple sclerosis was developed. These utility values allow identification of a hierarchic priority among different aspects of quality of life and may allow physicians to develop a care programme tailored to patient needs.
Log in om toegang te krijgen
Met onderstaand(e) abonnement(en) heeft u direct toegang:
Trask, P. C., Hsu, M. A., & McQuellon, R. (2009). Other paradigms: Health-related quality of life as a measure in cancer treatment: its importance and relevance. Cancer Journal, 15(5), 435–440. CrossRef
Miller, D. K. R. (2008). Health-related quality of life assessment in multiple sclerosis. Reviews in Neurological Diseases, 5(2), 56–64. PubMed
Mayo, N. E., Hum, S., & Kuspinar, A. (2012). Methods and measures: What’s new for MS? Mult Scler (Houndmills, Basingstoke, England), 19(6), 709–713. CrossRef
Hensher, D. A., Rose, J. M., & Greene, W. H. (2005). Applied choice analysis: A primer. Cambridge: Cambridge University Press. CrossRef
Ryan, M., Bate, A., Eastmond, C. J., & Ludbrook, A. (2001). Use of discrete choice experiments to elicit preferences. Quality Health Care, 10(Suppl 1), i55–i60. CrossRef
Ryan, M., & Gerard, K. (2003). Using discrete choice experiments to value health care programmes: Current practice and future research reflections. Applied Health Economics and Health Policy, 2(1), 55–64. PubMed
The EuroQoL Group. (1990). EuroQoL-a new facility for the measurement of health-related quality of life. Health Policy, 16, 199–208. CrossRef
Buchanan, R. J., Johnson, O., Zuniga, M. A., Carrillo-Zuniga, G., & Chakravorty, B. J. (2012). Health-related quality of life among Latinos with multiple sclerosis. Journal of Social Work in Disability & Rehabilitation, 11(4), 240–257. CrossRef
McFadden, D. (1974). The measurement of urban travel demand. Journal of Public Economics, 3, 303–328. CrossRef
Train, K. E. (2009). Discrete choice methods with simulation. Cambridge University Press.
Shingler, S. L., Swinburn, P., Ali, S., Perard, R., & Lloyd, A. J. (2013). A discrete choice experiment to determine patient preferences for injection devices in multiple sclerosis. Journal of Health Economics, 16(8), 1036–1042.
Benedict, R. H., Wahlig, E., Bakshi, R., Fishman, I., Munschauer, F., Zivadinov, R., et al. (2005). Predicting quality of life in multiple sclerosis: Accounting for physical disability, fatigue, cognition, mood disorder, personality, and behavior change. Journal of the Neurological Sciences, 231(1–2), 29–34. PubMedCrossRef
Kuhfeld, W. F. (2005). Marketing research methods in SAS. Experimental design, choice, conjoint, and graphical techniques. Cary, NC: SAS-Institute TS-722.
Orme, B. (1998). Sample size issues for conjoint analysis studies. In S. Software (Ed.), Sawtooth software: Research paper series.
Daly, A. J., & Hess, S. (2010). October). In European transport conference, Glasgow: Simple approaches for random utility modelling with panel data.
Greene, W. H. (2007). NLOGIT version 4.0: Reference guide. Plainview, NY: Econometric Software.
Institute, S. A. S. (1990). The SAS system for windows. Cary, North Carolina: SAS Institute Inc.
Aspinall, P. A., Johnson, Z. K., Azuara-Blanco, A., Montarzino, A., Brice, R., & Vickers, A. (2008). Evaluation of quality of life and priorities of patients with glaucoma. Investigative Ophthalmology & Visual Science, 49(5), 1907–1915. CrossRef
Tsevat, J. (2000). What do utilities measure? Medical Care, 38(Suppl 9), II160–164. PubMed
Zimbardo, P. G., & Boyd, J. N. (1999). Putting time in perspective: A valid, reliable individual-differences metric. Journal of Personality and Social Psychology, 77(6), 1271. CrossRef
Boniwell, I., & Zimbardo, P. G. (2004). Balancing one’s time perspective in pursuit of optimal functioning. In P. A. Linley & S. Joseph (Eds.), Positive Psychology in Practice (pp. 165–178). Hoboken, NJ: Wiley.
- Quality of life and patient preferences: identification of subgroups of multiple sclerosis patients
- Springer International Publishing