Mapping Algorithms For Predicting EQ-5D Utilities From The Assessment Test Of Chronic Obstructive Pulmonary Disease

Abstract

Objectives: In order to predict the European Quality of Life-5 Dimensions three-Level (EQ-5D-3L) questionnaire utility from the chronic obstructive pulmonary disease (COPD) assessment test (CAT).Methods: The EQ-5D-3L and CAT data from 323 patients were collected. At first, response mapping under a backward elimination procedure was used for the EQ-5D score predictions from the CAT scores. A multinomial logistic regression (MLR) model was used to identify the association between the score and the covariates using a minimized quasi-information criterion (QIC). Afterwards, the predicted scores were transformed to the utility. Validation in the model selection depended on the mean absolute error (MAE) and the root mean squared error (RMSE) for the validation group. We also compared the developed formula with previous models based on an ordinary least squares (OLS) regression. Results: Using response mapping with the MLR model to predict EQ-5D utility from CAT in this study performed as well as OLS regression models in previous studies using MAE and RMSE evaluations (all MAEs ≤ 0.100). In addition, the overestimation for low utility patients (utility ≤ 0.6) and underestimation for near health (utility > 0.9) in previous developed OLS models was improved in this study using a bubble chart analysis. Conclusions: Response mapping with the MLR model led to performance comparable to that using the OLS model for predicting EQ-5D utility from CAT data. In addition, the bubble charts revealed that the model constructed in this study was a better predictive model than other alternatives.