Background: Walking impairment has a major influence on the quality of life of people with multiple sclerosis (MS). The Multiple Sclerosis Walking Scale (MSWS-12) assesses the impact of MS on walking ability from the patient’s perspective, but in its current form, is not amenable for use in many policy decision-making settings.
Objectives: Statistical ‘mapping’ methods were used to convert MSWS-12 scores to EQ-5D health state values.
Methods: The relationship between the measures was estimated using cohort data from people with MS in South West England. Regression analyses were conducted, estimation errors assessed, and predictive performance of the best models tested using longitudinal data.
Results: Model performance was in line with that of other mapping studies, with the best-performing models being an ordinary least squares (OLS) model using MSWS-12 item scores, and an OLS model using the total MSWS-12 score and its squared term.
Conclusions: A process has been described whereby data from a patient-reported outcome measure (MSWS-12) can be converted to (EQ-5D) health state values. These values may be used to consider the health-related quality of life of people with MS, to estimate quality adjusted life-years for use in effectiveness and cost-effectiveness analyses, and to inform health policy decisions.
HeesenCBohmJReichCKasperJGoebelMGoldS. Patient perception of bodily functions in multiple sclerosis: gait and visual function are the most valuable. Mult Scler2008; 14: 988–991.
2.
SosnoffJGoldmanMDMotlRW. Real-life walking impairment in multiple sclerosis: preliminary comparison of four methods for processing accelerometry data. Mult Scler2010; 16: 868–877.
3.
Royal College of Physicians MT. National audit of services for people with multiple sclerosis. London: RCP, 2008.
4.
FinlaysonMVan DenendT. Experiencing the loss of mobility: perspectives of older adults with MS. Disab Rehabil2003; 25: 1168–1180.
5.
KurtzkeJ. Rating neurologic impairment in multiple sclerosis: an Expanded Disability Status Scale (EDSS). Neurology1983; 33: 1444–1452.
6.
FischerJJakAKnikerJRudickR. Multiple Sclerosis Functional Composite (MSFC): administration and scoring manual. New York: National Multiple Sclerosis Society, 2001.
7.
HobartJRiaziALampingDFitzpatrickRThompsonA. Measuring the impact of MS on walking ability: the 12-item MS Walking Scale (MSWS-12). Neurology2003; 60: 31–36.
8.
US Department of Health and Human Services Food and Drug Administration. Guidance for Industry. Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims. Silver Spring, USA, 2009.
9.
MillerDRudickR. Patient-centred outcomes: translating clinical efficacy into benefits on health-related quality of life. Neurology2010; 74: S24–S35.
10.
GoodmanABrownTKruppL. Sustained-release oral fampridine in multiple sclerosis: a randomised, double-blind, controlled trial. Lancet2009; 373: 732–738.
11.
ClinicalTrials.gov. Efficacy, safety and tolerability of nerispirdine in patients with multiple sclerosis. 2011.
12.
FreemanJGearMPauliA. The effect of core stability training on balance and mobility in ambulant individuals with multiple sclerosis: a multi-centre series of single case studies. Mult Scler2010; 16: 1377–1384.
13.
RamdharryGMarsdenJDayBThompsonA. De-stablizing and training effects of foot orthoses in multiple sclerosis. Mult Scler2006; 12: 219–226.
14.
BrazierJRatcliffeJSalomonJTsuchiyaA. Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press, 2007.
15.
DorseyEMeltzerD. The economics of comparative effectiveness research. Neurology2010; 75: 492-493.
16.
RigelSSwashM. Money and medicine: a problem that won’t go away. Neurology2009; 72: 766–768.
17.
GoldMSiegelJRussellLWeinsteinM. Cost-effectiveness in health and medicine. Oxford: Oxford University Press, 1996.
18.
RawlinsMCulyerA. National Institute for Clinical Excellence and its value judgments. Br Med J2004; 329: 224–227.
19.
Pharmaceutical Benefits Advisory Committee. Guidelines for preparing submissions to the Pharmaceutical Benefits Advisory Committee (Version 4.3). Barton, Australia: Australian Government, Department of Health and Ageing, 2008.
20.
Canadian Agency for Drugs and Technologies in Health (CADTH). Guidelines for the economic evaluation of health technologies. 3rd edition. Ottawa, Canada, 2006.
21.
GlickHDoshiJSonnadSPolskyD. Economic evaluation in clinical trials. Oxford: Oxford University Press, 2007.
22.
EuroQol Group. EQ-5D user guide. Rotterdam, Netherlands: The EuroQol Group, 1996.
23.
ZajicekJIngramWVickeryJCreanorSWrightDHobartJ. Patient-orientated longitudinal study of multiple sclerosis in south west England (The South West Impact of Multiple Sclerosis Project, SWIMS) 1: protocol and baseline characteristics of cohort. BMC Neurol2010; 10: 88.
24.
MotlRSnookE. Confirmation and extension of the validity of the Multiple Sclerosis Walking Scale-12 (MSWS-12). J Neurol Sci2008; 268: 69–73.
25.
HobartJCanoS. Improving the evaluation of therapeutic interventions in multiple sclerosis: the role of new psychometric methods. Health Technol Assess2009; 13: 1–216.
26.
DolanP. Modeling Valuations for EuroQol Health States. Med Care1997; 35: 1095–1108.
27.
National Institute for Health and Clinical Excellence. Guide to the methods of technology appraisal. London: National Institute for Health and Clinical Excellence, 2008.
28.
CrottRBriggsA. Mapping the QLQ-C30 quality of life cancer questionnaire to EQ-5D patient preferences. Eur J Health Econ2010; 11.
29.
XieFPullenyegumELiS-CHopkinsRThumbooJLoN-N. Use of a disease-specific instrument in economic evaluations: Mapping WOMAC onto the EQ-5D Utility Index. Value Health2010; 8: 873–878.
30.
BrennanDSpencerA. Mapping oral health related quality of life to generic health state values. BMC Health Serv Res2006; 6: 96.
31.
BuxtonMLaceyLFeaganBOliverR. Mapping from disease-specific measures to utility: an analysis of the relationship between the Inflammatory Bowel Disease Questionnaire and Crohn’s Disease Activity Index in Crohn’s disease and measures of utility. Value Health2007; 10: 214–220.
32.
CheungYTanLLauPAuWLuoN. Mapping the eight-item Parkinson’s Disease Questionnaire (PDQ-8) to the EQ-5D Utility Index. Qual Life Res2008; 17: 1173–1181.
33.
BrazierJYangYTsuchiyaARowenD. A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. Eur J Health Econ2010; 11: 215–225.
34.
BrazierJKolotkinRCrosbyRWilliamsG. Estimating a preference-based single index for the Impact of Weight on Quality of Life-Lite (IWQOL-Lite) instrument from the SF-6D. Value Health2004; 7: 490–498.
35.
LongJFreeseJ. Regression models for categorical and limited dependent variables. Thousand Oaks, CA: Sage Publications, 1997.
36.
PowellJ. Least absolute deviations estimation for the censored regression model. J Econom1984; 25: 303–325.
37.
RowenDBrazierJRobertsJ. Mapping SF-36 onto the EQ-5D index: how reliable is the relationship?Health Qual Life Outcomes2009; 7: 27.
38.
VersteeghMRowenDBrazierJStolkE. Mapping onto EQ-5D for patients in poor health. Health Qual Life Outcomes2010; 8: 141.
39.
PhillipsCHumphreysI. Assessing cost-effectiveness in the management of multiple sclerosis. ClinicoEcon Outcomes Res2009; 1: 61–78.
40.
NaciHFleurenceRBirtJDuhigA. The impact of increasing neurological disability of multiple sclerosis on health utilities: a systematic review of the literature. J Med Econ2010; 13: 78–89.
41.
HobartJCanoSZajicekJThompsonA. Rating scales as outcome measures for clinical trials in neurology: problems, solutions, and recommendations. Lancet Neurol2007; 6: 1094–1105.
42.
RoundJ. Capturing information loss in estimates of uncertainty that arise from mapping algorithms. London: Health Economists’ Study Group, 2008.
43.
ParkerMHaycoxAGravesJ. Estimating the relationship between preference-based generic utility instruments and disease-specific quality-of-life measures in severe chronic constipation: Challenges in practice. Pharmacoeconomics2011; 10.2165/1158836010.2165/11588360.
44.
DakinHPetrouSHaggardMBengeSWilliamsonI. Mapping analyses to estimate health utilities based on responses to the OM8-30 otitis media questionnaire. Qual Life Res2010; 19: 65–80.
45.
ProsserLKuntzKBar-OrAWeinsteinM. Patient and community preferences for treatments and health states in multiple sclerosis. Mult Scler2003; 9: 311–319.
