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Mapping Oswestry Disability Index Responses to EQ-5D-3L Utility Values: Are Cost-Utility Results Valid?
Ângela Jornada Ben1, Sylvia Pellekooren2, Judith E Bosmans1
1Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
Objectives:
To develop and validate approaches for mapping Oswestry Disability Index responses to 3-level version of EQ-5D utility values and to evaluate the impact of using mapped utility values on cost-utility results compared with published regression models.
Methods:
Three response mapping approaches were developed in a random sample of 70% of 18 692 patients with low back pain: nonparametric approach (Non-p), nonparametric approach excluding logical inconsistencies (Non-peLI), and ordinal logistic regression (OLR). Performance was assessed in the remaining 30% using R-square (R2), root mean square error (RMSE), and mean absolute error (MAE). To evaluate whether MAEs and their 95% limits of agreement (LA) were clinically relevant, a minimally clinically important difference of 0.074 was used. Probabilities of cost-effectiveness estimated using observed and mapped utility values were compared in 2 economic evaluations.
Results:
The Non-p performed the best (R2 = 0.43; RMSE = 0.22; MAE = 0.03; 95% LA = -0.40 to 0.47) compared with the Non-peLI (R2 = 0.07; RMSE = 0.29; MAE = -0.15; 95% LA = -0.63 to 0.34) and OLR (R2 = 0.22; RMSE = 0.26; MAE = 0.02; 95% LA = -0.49 to 0.53). MAEs were lower than the minimally clinically important difference for the Non-p and OLR but not for the Non-peLI. Differences in probabilities of cost-effectiveness ranged from 1% to 4% (Non-p), 0.1% to 9% (Non-peLI), and 0.1% to 20% (OLR).
Conclusions:
Results suggest that the developed response mapping approaches are not valid for estimating individual patients' 3-level version of EQ-5D utility values, and-depending on the approach-may considerably affect cost-utility results. The developed approaches did not perform better than previously published regression-based models and are therefore not recommended for use in economic evaluations.
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