Date: 15 Dec 2013
Using existing data to identify candidate items for a health state classification system in multiple sclerosis
Rent the article at a discountRent now
* Final gross prices may vary according to local VAT.Get Access
In multiple sclerosis (MS), the use of preference-based measures is limited to generic measures such as Health Utilities Index Mark 2 and 3, the EQ-5D and the SF-6D. However, the challenge of using such generic preference-based measures in people with MS is that they may not capture all domains of health relevant to the disease. Therefore, the main aim of this paper is to describe the development of a health state classification system for MS patients. The specific objectives are: (1) to identify items best reflecting the domains of quality of life important to people with MS and (2) to provide evidence for the discriminative capacity of the response options by cross-walking onto a visual analog scale of health rating.
The data come from an epidemiologically sampled population of people with MS diagnosed post-1994. The dataset consisted of 206 items relating to impairments, activity limitations, participation restrictions, health perception and quality of life. Important domains were identified from the responses to the Patient Generated Index, an individualized measure of quality of life. The extent to which the items formed a uni-dimensional, linear construct was estimated using Rasch analysis, and the best item was selected using the threshold map.
The sample was young (mean age 43) and predominantly female (n = 140/189; 74 %). The P-PBMSI classification system consisted of five items, with three response levels per item, producing a total of 243 possible health states. Regression coefficient values consistently decreased between response levels and the linear test for trend were statistically significant for all items. The linear test for trend indicated that for each item the response options provided the same discriminative ability within the magnitude of their capacity. A scoring algorithm was estimated using a simple additive formula. The classification system demonstrated convergent validity against other measures of similar constructs and known-groups validity between different clinical subgroups.
This study produced a health state classifier system based on items impacted upon by MS, and demonstrated the potential to discriminate the health impact of the disease.
Miller, D. M., Moore, S. M., Fox, R. J., Atreja, A., Fu, A. Z., Lee, J. C., et al. (2011). Web-based self-management for patients with multiple sclerosis: A practical, randomized trial. Telemedicine Journal & E-Health, 17, 5–13.CrossRef
Bombardier, C. H., Cunniffe, M., Wadhwani, R., Gibbons, L. E., Blake, K. D., & Kraft, G. H. (2008). The efficacy of telephone counseling for health promotion in people with multiple sclerosis: A randomized controlled trial. Archives of Physical Medicine and Rehabilitation, 89, 1849–1856.PubMedCrossRef
Freedman, M. S., Bar-Or, A., Atkins, H. L., Karussis, D., Frassoni, F., Lazarus, H., et al. (2010). The therapeutic potential of mesenchymal stem cell transplantation as a treatment for multiple sclerosis: Consensus report of the International MSCT Study Group. Multiple Sclerosis, 16, 503–510.PubMedCrossRef
Al-Omari, M. H., & Rousan, L. A. (2010). Internal jugular vein morphology and hemodynamics in patients with multiple sclerosis. International Angiology, 29, 115–120.PubMed
Torrance, G. W. (1986). Measurement of health state utilities for economic appraisal. J Health Economics, 5, 1–30.CrossRef
Brazier, J., Ratcliffe, J., Salomon, J. A., & Tsuchiya, A. (2007). Measuring and valuing health benefits for economic evaluation. New York: Oxford University Press Inc.
Kind, P. (2005). Values and valuation in the measurement of HRQoL. In P. Fayers & D. Hays (Eds.), Assessing quality of life in clinical trials (pp. 391–404). New York: Oxford University Press Inc.
Feeny, D., Torrance, G. W., & Furlong, W. (1996). Health utilities index. In B. Spilker (Ed.), Quality of life and pharmaeconomics in clinicals trials (pp. 239–252). Philadelphia: Lippincott-Raven Publishers.
Feeny, D. (2005). Preference-based measures: Utility and quality-adjusted life years. In P. Fayers & D. Hays (Eds.), Assessing quality of life in clinical trials (pp. 405–429). New York: Oxford University Press Inc.
Berzon, R., Mauskopf, J. A., & Simeon, G. P. (1996). Choosing a health profile (descriptive) and/or a patient-preference (utility) measure for a clinical trial. In B. Spilker (Ed.), Quality of life and pharmaeconomics in clinical trials (pp. 375–379). Philadelphia: Lippincott-Raven Publishers.
Hawthorne, G., & Richardson, J. (2001). Measuring the value of program outcomes: A review of multiattribute utility measures. Expert Review of Pharmacoeconomics & Outcomes Research, 1, 215–228.CrossRef
Williams, A. (2005). The EuroQol instrument. In P. Kind, R. Brooks, & R. Rabin (Eds.), EQ-5D concepts and methods: A developmental history (pp. 1–17). Dordrecht: Springers.CrossRef
Pickard, A. S., Shaw, J. W., Lin, H. W., Trask, P. C., Aaronson, N., Lee, T. A., et al. (2009). A patient-based utility measure of health for clinical trials of cancer therapy based on the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire. Value Health, 12, 977–988.PubMedCrossRef
McKenna, S. P., Ratcliffe, J., Meads, D. M., & Brazier, J. E. (2008). Development and validation of a preference based measure derived from the Cambridge Pulmonary Hypertension Outcome Review (CAMPHOR) for use in cost utility analyses. Health and Quality of Life Outcomes, 6, 65.PubMedCentralPubMedCrossRef
Lin, F. J., Longworth, L., Pickard, A. S. (2013). Evaluation of content on EQ-5D as compared to disease-specific utility measures. Quality Life Research, 22(4), 853–874.
Ruta, D. A., Garratt, A. M., & Russell, I. T. (1999). Patient centred assessment of quality of life for patients with four common conditions. Quality Health Care, 8, 22–29.CrossRef
Sullivan, M. J., Edgley, K., & Dehoux, E. (1990). A survey of multiple sclerosis: I. Perceived cognitive problems and compensatory strategy use. Canadian Journal of Rehabilitation, 4, 99–105.
Schumacker, R. E. (1996). Editor’s note. Structural Equation Modeling: A Multidisciplinary Journal, 3, 1–2.CrossRef
Wright, B. D. (1996). Comparing Rasch measurement and factor analysis. Structural Equation Modeling: A Multidisciplinary Journal, 3, 3–24.CrossRef
Chang, C. H. (1996). Finding two dimensions in MMPI-2 depression. Structural Equation Modeling: A Multidisciplinary Journal, 3, 41–49.CrossRef
Andrich, D., Lyne, A., Sheridan, B., & Luo, G. (2004). Rasch unidimensional measurement models (RUMM) 2020. Perth, Western Australia: Rumm Laboratory Pty Ltd.
Bond, T. G., & Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human science. New Jersey: Lawrence Erlbaum Associates Inc.
Allen, M. J., & Yen, W. M. (2002). Introduction to measurement theory. Long Grove: Waveland Press Inc.
Broome, H. (2012) The association between cognition, social functioning, physical impairment, and relationship factors in individuals with multiple sclerosis (pp. 1–195). The University of Hull.
Lee, E. K. O., & Oh, H. (2012). Marital satisfaction among adults with disabilities in South Korea. Journal of Disability Studies Policy, 23, 215–224.CrossRef
Fisk, J. D., Pontefract, A., Ritvo, P. G., Archibald, C. J., & Murray, T. J. (1994). The impact of fatigue on patients with multiple sclerosis. Canadian Journal of Neurological Sciences, 21, 9–14.PubMed
Freal, J. E., Kraft, G. H., & Coryell, J. K. (1984). Symptomatic fatigue in multiple sclerosis. Archives of Physical Medicine and Rehabilitation, 65, 135–138.PubMed
Murray, T. J. (1985). Amantadine therapy for fatigue in multiple sclerosis. Canadian Journal of Neurological Sciences, 12, 251–254.PubMed
Stallard, J., & Major, R. E. (1995). The influence of orthosis stiffness on paraplegic ambulation and its implications for functional electrical stimulation (FES) walking systems. Prosthetics and Orthotics International, 19, 108–114.PubMed
Brissot, R., Gallien, P., Le Bot, M. P., Beaubras, A., Laisne, D., Beillot, J., et al. (2000). Clinical experience with functional electrical stimulation-assisted gait with Parastep in spinal cord-injured patients. Spine (Phila Pa 1976), 25, 501–508.CrossRef
Rothman, M., Burke, L., Erickson, P., Leidy, N. K., Patrick, D. L., & Petrie, C. D. (2009). Use of existing patient-reported outcome (PRO) instruments and their modification: The ISPOR good research practices for evaluating and documenting content validity for the use of existing instruments and their modification PRO task force report. Value Health, 12, 1075–1083.PubMedCrossRef
Food, U. S. (2009). Drug administration: Guidance for industry: Patient-reported outcome measures: Use in medical product development to support labeling claims. Federal Register, 74, 65132–65133.
- Using existing data to identify candidate items for a health state classification system in multiple sclerosis
Quality of Life Research
Volume 23, Issue 5 , pp 1445-1457
- Cover Date
- Print ISSN
- Online ISSN
- Springer International Publishing
- Additional Links
- Health-related quality of life
- Preference-based measures
- Multiple sclerosis
- Industry Sectors
- Author Affiliations
- 1. Faculty of Medicine, School of Physical and Occupational Therapy, McGill University, 3654 Prom Sir William Osler, Montreal, QC, H3G 1Y5, Canada
- 2. Division of Clinical Epidemiology, McGill University Health Center, Montreal, QC, Canada
- 3. Department of Pharmacy Systems, Outcomes and Policy, Center for Pharmacoepidemiology and Pharmacoeconomic Research, University of Illinois at Chicago, Chicago, IL, USA