Abstract

Background: Mappingfromnon-performance-based measures onto generic performance-based measures providesanappropriate solution to derive utilities to be used in economic evaluations. Objectives: This study aimed to create a model through which EQ-5D utilities for cataracts can be obtained from scores on the disease-specific Catquest measure. Patients and Methods: One hundred ninety-nine observations from 103 patients who self-administered the EQ-5D, the Catquest and questions on demographic and clinical characteristics were included in the analysis. Data was divided into estimation and validation datasets. To predict EQ-5D utilities, multiple regression analysis, using the Ordinary Least Square (OLS) and the censored least absolute deviation (CLAD), was performed. Catquest scores, age, gender, and performing surgery were included as explanatory variables. An estimation dataset was used to derive the coefficients, and these coefficients were then validated using a validation dataset. Basedonthe explanatorypower, the consistency, the simplicity, themeanabsolute error (MAE)andthe correlationsbetween observed and fitted utilities, the most appropriate model was selected. Results: The mean EQ-5D and Catquest scores of the total sample were 0.631 and 15.8, respectively. Age and surgery showed no significant effect for either method. Removing age and surgery, model II was built and given an R2 of 0.697, an MAE of 0.1176 for the OLS and an R2 of 0.614, and an MAE of 0.1153 for the CLAD method. In the validation stage, the CLAD revealed better prediction ability, with an MAE of 0.198 versus an MAE of 0.209 for the OLS. ICC and Bland-Altman analysis put the CLAD as a preferred method with the following equation: Utilities (EQ-5D) = 0.988 - 0.0281Catquest (PD) + 0.102gender (male = 1). Conclusions: Based on these results, a mapping function was obtained which appears to be valuable in predicting EQ-5D utilities from Catquest scores. This function gives an appropriate solution to estimate utilities when primary EQ-5D data is not available. Although the model represents good consistency and predictive ability, further examination of obtained function is required with large samples.

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