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• W2116769108 abstract "Background. Mapping disease-specific instruments into generic health outcomes or utility values is an expanding field of interest in health economics. This article constructs an algorithm to translate the modified Rankin scale (mRS) into EQ-5D utility values. Methods. mRS and EQ-5D information was derived from stroke or transient ischemic attack (TIA) patients identified as part of the Oxford Vascular study (OXVASC). Ordinary least squares (OLS) regression was used to predict UK EQ-5D tariffs from mRS scores. An alternative method, using multinomial logistic regression with a Monte Carlo simulation approach (MLogit) to predict responses to each EQ-5D question, was also explored. The performance of the models was compared according to the magnitude of their predicted-to-actual mean EQ-5D tariff difference, their mean absolute and mean squared errors (MAE and MSE), and associated 95% confidence intervals (CIs). Out-of-sample validation was carried out in a subset of coronary disease and peripheral vascular disease (PVD) patients also identified as part of OXVASC but not used in the original estimation. Results. The OLS and MLogit yielded similar MAE and MSE in the internal and external validation data sets. Both approaches also underestimated the uncertainty around the actual mean EQ-5D tariff producing tighter 95% CIs in both data sets. Conclusions. The choice of algorithm will be dependent on the study aim. Individuals outside the United Kingdom may find it more useful to use the multinomial results, which can be used with different country-specific tariff valuations. However, these algorithms should not replace prospective collection of utility data." @default.
• W2116769108 created "2016-06-24" @default.
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• W2116769108 date "2009-10-26" @default.
• W2116769108 modified "2023-10-13" @default.
• W2116769108 title "Mapping the Modified Rankin Scale (mRS) Measurement into the Generic EuroQol (EQ-5D) Health Outcome" @default.
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• W2116769108 doi "https://doi.org/10.1177/0272989x09349961" @default.
• W2116769108 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/19858500" @default.
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