Abstract
ObjectivesTo develop algorithms mapping the Kidney Disease Quality of Life 36-Item Short Form Survey (KDQOL-36) onto the 3-level EQ-5D questionnaire (EQ-5D-3L) and the 5-level EQ-5D questionnaire (EQ-5D-5L) for patients with end-stage renal disease requiring dialysis.MethodsWe used data from a cross-sectional study in Europe (France, n = 299; Germany, n = 413; Italy, n = 278; Spain, n = 225) to map onto EQ-5D-3L and data from a cross-sectional study in Singapore (n = 163) to map onto EQ-5D-5L. Direct mapping using linear regression, mixture beta regression and adjusted limited dependent variable mixture models (ALDVMMs) and response mapping using seemingly unrelated ordered probit models were performed. The KDQOL-36 subscale scores, i.e., physical component summary (PCS), mental component summary (MCS), three disease-specific subscales or their average, i.e., kidney disease component summary (KDCS), and age and sex were included as the explanatory variables. Predictive performance was assessed by mean absolute error (MAE) and root mean square error (RMSE) using 10-fold cross-validation.ResultsMixture models outperformed linear regression and response mapping. When mapping to EQ-5D-3L, the ALDVMM model was the best-performing one for France, Germany and Spain while beta regression was best for Italy. When mapping to EQ-5D-5L, the ALDVMM model also demonstrated the best predictive performance. Generally, models using KDQOL-36 subscale scores showed better fit than using the KDCS.ConclusionsThis study adds to the growing literature suggesting the better performance of the mixture models in modelling EQ-5D and produces algorithms to map the KDQOL-36 onto EQ-5D-3L (for France, Germany, Italy, and Spain) and EQ-5D-5L (for Singapore).
Highlights
The number of patients with end-stage renal disease (ESRD) is projected to increase substantially, driven by the ageing population and the rising number of people with diabetes, hypertension, and obesity [1, 2]
This concern has been supported by one recently published study which reported that the EQ-5D scores mapped from Short Form 12-Item (SF-12) would underestimate the quality-adjusted life years (QALYs) gained in cost-utility analysis compared to the observed EQ-5D [16], and there is a necessity for developing new methods to enable better health utility estimates from KDQOL-36 data for future economic evaluations in dialysis patients when EQ-5D data are not available
As there is no overall KDQOL36 score that incorporates all of its subscale scores, the following scores were calculated separately: physical component summary (PCS), mental component summary (MCS), burden of kidney disease (Burden), symptoms/problems of kidney disease (Symptoms), and effects of kidney disease (Effect), using the Excel file provided by the RAND Corporation [19]; and kidney disease component summary (KDCS) was calculated by averaging the three disease-specific subscale scores
Summary
The number of patients with end-stage renal disease (ESRD) is projected to increase substantially, driven by the ageing population and the rising number of people with diabetes, hypertension, and obesity [1, 2]. One alternative approach is to use the currently available mapping algorithms from SF-12 onto EQ-5D [12,13,14,15], but these algorithms do not show the complete picture of KDQOL-36 (only includes 12 items of KDQOL36) and may not produce the reliable estimates This concern has been supported by one recently published study which reported that the EQ-5D scores mapped from SF-12 would underestimate the QALYs gained in cost-utility analysis compared to the observed EQ-5D [16], and there is a necessity for developing new methods to enable better health utility estimates from KDQOL-36 data for future economic evaluations in dialysis patients when EQ-5D data are not available
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