Abstract
BackgroundFatigue has a major influence on the quality of life of people with multiple sclerosis. The Fatigue Severity Scale is a frequently used patient-reported measure of fatigue impact, but does not generate the health state utility values required to inform cost-effectiveness analysis, limiting its applicability within decision-making contexts. The objective of this study was to use statistical mapping methods to convert Fatigue Severity Scale scores to health state utility values from three preference-based measures: the EQ-5D-3L, SF-6D and Multiple Sclerosis Impact Scale-8D.MethodsThe relationships between the measures were estimated through regression analysis using cohort data from 1056 people with multiple sclerosis in South West England. Estimation errors were assessed and predictive performance of the best models as tested in a separate sample (n = 352).ResultsFor the EQ-5D and the Multiple Sclerosis Impact Scale-8D, the best performing models used a censored least absolute deviation specification, with Fatigue Severity Scale total score, age and gender as predictors. For the SF-6D, the best performing model used an ordinary least squares specification, with Fatigue Severity Scale total score as the only predictor.ConclusionsHere we present algorithms to convert Fatigue Severity Scales scores to health state utility values based on three preference-based measures. These values may be used to estimate quality-adjusted life-years for use in cost-effectiveness analyses and to consider the health-related quality of life of people with multiple sclerosis, thereby informing health policy decisions.
Highlights
Fatigue has a major influence on the quality of life of people with multiple sclerosis
This experience was clearly of great importance to the people with Multiple Sclerosis (MS) who contributed to the original research, and underpinned the ways in which fatigue impacts upon health-related quality of life (HRQoL)
Results of mapping analysis Exploration of model specifications In order to allow for heteroscedasticity, skewness and kurtosis identified in the data, we fitted robust ordinary least squares (OLS) models and used a Censored Least Adjusted Deviation (CLAD) rather than a Tobit specification. (The distribution of scores on each of the measures, and the relationships between scores on the preference -based measure (PBM) and the Fatigue Severity Scale (FSS) total score is shown in the Additional file 2 B and Additional file 3: C)
Summary
Fatigue has a major influence on the quality of life of people with multiple sclerosis. The Fatigue Severity Scale (FSS) [3] is frequently used in clinical trials of interventions for fatigue in people with MS, including carnitine, amantadine, aspirin, modafinil and cognitive behavioural therapy [4,5,6,7]. Symptomspecific outcome measures, such as the FSS, provide a standardised means of describing “health states” that may Goodwin et al Health and Quality of Life Outcomes (2019) 17:136 be experienced by patients, but do not provide data in the format required by many decision-making bodies to assess cost-effectiveness [1]
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