The use of multiple sclerosis condition-specific measures to inform health policy decision-making: mapping from the MSWS-12 to the EQ-5D

Hawton, Annie; Green, C; Telford, CJ; Wright, D. E.; Zajicek, JP

doi:10.1177/1352458511429319

Cited by 16 publications

(14 citation statements)

References 36 publications

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“…10 Hawton et al 10 used data from people with MS in the South West of England project. Similar to our analysis, they used OLS regression modelling, using all individual MSWS-12 item scores (except for item 11, which was removed due to collinearity) to provide the best performing equation (MAE=0.148, RMSE=0.198 and adjusted R 2 =0.361).…”

Section: Discussionmentioning

confidence: 99%

“…For the latter analyses, participants' mobility impairment was categorised using the PDDS 12 with scores of ≤2, 3 to 6, and ≥7 representing 'no', 'moderate' and 'severe/total' mobility impairment, respectively; the NARCOMS mobility performance scale 13 (rated on a scale ranging between 0, 'normal' and 6 'total disability'); and the EQ-5D (<0.50, 0.50 to <0.75 and ≥0.75). 10 Descriptive statistics are reported as percentages for categorical data and mean (SD) for continuous data. A p value of <0.05 was considered statistically significant in all situations.…”

Section: Methodsmentioning

confidence: 99%

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Mapping the 12-item multiple sclerosis walking scale to the EuroQol 5-dimension index measure in North American multiple sclerosis patients

et al. 2013

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ObjectiveTo map the 12-item Multiple Sclerosis Walking Scale (MSWS-12) onto the EuroQol 5-dimension (EQ-5D) health-utility index in multiple sclerosis (MS) patients participating in the North American Research Committee on Multiple Sclerosis (NARCOMS) registry.DesignCross-sectional MSWS-12 to EQ-5D cross-walking analysis.SettingNARCOMS registry spring 2010 biannual update and supplemental survey.ParticipantsNorth American patients completing both the MSWS-12 and the EQ-5D randomly split into derivation and validation cohorts.Outcome measuresOrdinary least squares regression was performed within the derivation cohort, with participants’ EQ-5D as the dependent variable. Results of the MSWS-12 were input as independent variable(s) into six regression models. Model goodness-of-fit was subsequently assessed in the validation cohort using the mean absolute error (MAE), root mean square error (RMSE) and the adjusted R2. The best performing model was refined in the entire cohort and utilised for additional analyses.ResultsA total of 3505 NARCOMS participants were included. Their mean±SD EQ-5D and MSWS-12 scores were 0.74±0.18 and 50.8±33.5, respectively, and these assessments were found to be moderately correlated (r=–0.553, p<0.001). The model using all individual MSWS-12 item scores as independent variables was found to have the best fit (MAE=0.109±0.096, RMSE=0.145, adjusted R2=0.329). The percentage of EQ-5D estimates within 0.05 and 0.10 of the actual value were 30% and 61%, respectively. This mapping equation was more precise in patients with moderate mobility impairment (MAE=0.087±0.061 at patient-determined disease step (PDDS) of 3–6) and less precise in patients with no (MAE=0.141±0.128 at PDDS of 0–2) or severe mobility impairment (MAE=0.121±0.049 at PDDS ≥7).ConclusionsThe EQ-5D scores can be predicted using the MSWS-12 item scores with reasonable precision in North American patients with MS. Prediction estimates were more precise in patients with moderate mobility impairment.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Mapping the 12-item multiple sclerosis walking scale to the EuroQol 5-dimension index measure in North American multiple sclerosis patients

et al. 2013

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“…A diagrammatic representation of the areas of joint coverage is given in Fig. 1'' [24]. ''We started our analyses by exploring the data to find the (dis)similarities between the two instruments using Spearman correlations.…”

Section: Item 8: Exploratory Data Analysismentioning

confidence: 99%

The MAPS Reporting Statement for Studies Mapping onto Generic Preference-Based Outcome Measures: Explanation and Elaboration

et al. 2015

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Background The process of ''mapping'' is increasingly being used to predict health utilities, for application within health economic evaluations, using data on other indicators or measures of health. Guidance for the reporting of mapping studies is currently lacking. Objective The overall objective of this research was to develop a checklist of essential items, which authors should consider when reporting mapping studies. The MAPS (MApping onto Preference-based measures reporting Standards) statement is a checklist, which aims to promote complete and transparent reporting by researchers. This paper provides a detailed explanation and elaboration of the items contained within the MAPS statement. Methods In the absence of previously published reporting checklists or reporting guidance documents, a de novo list of reporting items and accompanying explanations was created. A two-round, modified Delphi survey, with representatives from academia, consultancy, health technology assessment agencies and the biomedical journal editorial community, was used to identify a list of essential reporting items from this larger list. Results From the initial de novo list of 29 candidate items, a set of 23 essential reporting items was developed. The items are presented numerically and categorised within six sections, namely, (i) title and abstract, (ii) introduction, (iii) methods, (iv) results, (v) discussion and (vi) other. For each item, we summarise the recommendation, illustrate it using an exemplar of good reporting practice identified from the published literature, and provide a detailed explanation to accompany the recommendation. Conclusions It is anticipated that the MAPS statement will promote clarity, transparency and completeness of reporting of mapping studies. It is targeted at researchers developing mapping algorithms, peer reviewers and editors involved in the manuscript review process for mapping studies, and the funders of the research. The MAPS working group plans to assess the need for an update of the reporting checklist in 5 years' time. Each of the 23 items contained within the MAPS reporting statement is illustrated with an exemplar of good reporting practice identified from the published literature. In addition, a detailed explanation and elaboration is provided for each of the 23 reporting items.It is anticipated that the MAPS reporting statement will promote clarity, transparency and completeness of reporting of mapping studies.

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“…One group of authors asserted 27,28 ''We strongly believe that it is the structure of the instrument that causes this phenomenon rather than the fact that it appears to highlight 2 subgroups of patients.'' 28(p88) One study 36 examined this by seeking external validation of the groups identified by the EQ-5D distribution. In mapping a condition-specific measure (i.e., the MSWS-12) to the EQ-5D-3L, the authors calculated median MSWS-12 scores for 2 groups defined by a cutpoint, which was the EQ-5D-3L score closest to 0.5.…”

Section: The Shape Of Eq-5d Index Distributionsmentioning

confidence: 99%

“…30 Studies of other conditions and populations include accident victims with life-threatening injuries, 10 breast cancer, 31 chronically ill patients undergoing hemodialysis, 32 lower back pain, chronic obstructive pulmonary disease, end-stage renal disease, 10 irritable bowel syndrome, leg ulcer and osteoporosis, 8,9 menopausal women and healthy older women, 8 pregnant women with chronic energy deficiency, 33 HIV, 34 postmenopausal women, 35 multiple myeloma and non-Hodgkin's lymphoma, 7 asthma, chest pain, clodronate, hormone replacement therapy, leg reconstruction and varicose veins, 9 and multiple sclerosis. 36 It is often alleged that the gap arises because the decrement in the index between levels 2 and 3 is relatively large compared with that between levels 1 and 2.…”

Section: Introductionmentioning

confidence: 99%

What Determines the Shape of an EQ-5D Index Distribution?

2016

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Background. EQ-5D-3L index scores in patient and general populations typically have a nonnormal distribution, divided into 2 distinct groups. It is important to understand to what extent this is determined by the way that the EQ-5D-3L index is constructed rather than by the true distribution of ill health. Objective. This paper examines the determinants of the “2 groups” distribution pattern and the extent to which this pattern is attributable either to the EQ-5D-3L classification system used to create health state profiles or to the weights applied to profiles. Methods. Data from the English NHS PROMs program (hip and knee replacements and varicose vein and hernia repairs) and from a study of 2 chronic conditions (asthma and angina) were used to compare the distributions of EQ-5D-3L index scores with distributions from which weights have been stripped; profile data decomposed into their constituent dimensions and levels; a condition-specific index; and using weights from different countries, based on both time tradeoff and visual analogue scale. Results. The EQ-5D-3L classification system generates differences between patients with the same condition in respect of dimensions that are mainly observed at level 2 or 3. The weights commonly used to calculate the index exacerbate this grouping by placing a larger weight on level 3 observations, generating a noticeable gap in index scores between the groups. Conclusions. Analyzing EQ-5D profile data enables a better understanding of the resulting distribution of EQ-5D scores. The distinctive shape observed for these distributions is the result of both the classification system and the weights applied to it.

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The use of multiple sclerosis condition-specific measures to inform health policy decision-making: mapping from the MSWS-12 to the EQ-5D

Cited by 16 publications

References 36 publications

Mapping the 12-item multiple sclerosis walking scale to the EuroQol 5-dimension index measure in North American multiple sclerosis patients

Mapping the 12-item multiple sclerosis walking scale to the EuroQol 5-dimension index measure in North American multiple sclerosis patients

The MAPS Reporting Statement for Studies Mapping onto Generic Preference-Based Outcome Measures: Explanation and Elaboration

What Determines the Shape of an EQ-5D Index Distribution?

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