ArticlePharmacoEconomics - open2023
Mapping the World Health Organization Disability Assessment Schedule (WHODAS 2.0) onto SF-6D Using Swedish General Population Data.
Article in PharmacoEconomics - open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Norwegian and Swedish value sets for the EORTC QLU-C10D utility instrument.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
BACKGROUND AND
objectiveMapping algorithms can be used for estimating quality-adjusted life years (QALYs) when studies apply non-preference-based instruments. In this study, we estimate a regression-based algorithm for mapping between the World Health Organization Disability Assessment Schedule (WHODAS 2.0) and the preference-based instrument SF-6D to obtain preference estimates usable in health economic evaluations. This was done separately for the working and non-working populations, as WHODAS 2.0 discriminates between these groups when estimating scores.
methodsUsing a dataset including 2258 participants from the general Swedish population, we estimated the statistical relationship between SF-6D and WHODAS 2.0. We applied three regression methods, i.e., ordinary least squares (OLS), generalized linear models (GLM), and Tobit, in mapping onto SF-6D from WHODAS 2.0 at the overall-score and domain levels. Root mean squared error (RMSE) and mean absolute error (MAE) were used for validation of the models; R
resultsThe best-performing models for both the working and non-working populations were GLM models with RMSE ranging from 0.084 to 0.088, MAE ranging from 0.068 to 0.071, and R
conclusionsIt is possible to apply the derived mapping algorithms for health economic evaluations in studies using WHODAS 2.0. As conceptual overlap is incomplete, we recommend using the domain-based algorithms over the overall score. Different algorithms must be applied depending on whether the population is working or non-working, due to the characteristics of WHODAS 2.0.
Identifiers
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Registered trials
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