Trial reportQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation2025
Mapping the modified rankin scale (mRS) onto the assessment of quality of life (AQoL) utilities.
Trial report in Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundEconomic evaluations need data from preference-based measures (PBM) to calculate quality-adjusted life years (QALY). Clinical trials often do not collect this data, which has prompted the development of mapping techniques. One such utility instrument to measure quality of life is the Assessment of Quality of Life-4D(AQoL-4D) and a widely used measure to assess global disability in stroke trials is the modified Rankin Scale (mRS). Thus, mapping techniques to link the mRS score with a health-related quality of life (HRQoL) such as AQoL-4D, is useful for health economists working in stroke.
methodThe data were obtained from the AVERT trial (A Very Early Rehabilitation Trial after stroke), a pragmatic, prospective, parallel-group, multicentre, international, randomised control trial. Seven econometric models were used to map mRS onto AQoL-4D: ordinary least square (OLS), robust MM-estimator, Generalised linear model (GLM), Generalised additive model (GAM), beta-based mixture model (betamix), Tobit model, and adjusted limited dependent variable mixture model (aldvmm). Five-fold internal cross-validation was performed to assess internal validity. Model performance was assessed using a series of goodness-of-fit measures.
resultFour models were developed: Model 1a: using the mRS score, age and gender as the key predictive variable; Model 1b: Model 1a without age and gender as predictors; Model 2a: using the set of binary variables of mRS scores, age and gender as the key predictive variable and; Model 2b: Model 2a without the impact of age and gender as predictors. The robust MM estimator was the preferred mapping algorithm for the overall sample with the smallest mean absolute error in cross-validation (0.1368 and 0.1364) for models 2a and 2b, respectively, which was considered acceptable.
conclusionThe developed mapping algorithm enables cost-utility analyses of interventions for people with stroke where the data for mRS has been collected. The availability of this mapping algorithm will facilitate future economic evaluation for stroke interventions when only the mRS is used.
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