Do country-specific preference weights matter in the choice of mapping algorithms? The case of mapping the Diabetes-39 onto eight country-specific EQ-5D-5L value sets

Abstract

Purpose: To develop mapping algorithms that transform Diabetes-39 (D-39) scores onto EQ-5D-5L utility values for each of eight recently published country-specific EQ-5D-5L value sets, and to compare mapping functions across the EQ-5D-5L value sets.Methods: Data include 924 individuals with self-reported diabetes from six countries. The D-39 dimensions, age and gender were used as potential predictors for EQ-5D-5L utilities, which were scored using value sets from eight countries (England, Netherland, Spain, Canada, Uruguay, China, Japan and Korea). Ordinary least squares, generalized linear model, beta binomial regression, fractional regression, MM-estimation, and censored least absolute deviation were used to estimate the mapping algorithms. The optimal algorithm for each country-specific value set was primarily selected based normalized root mean square error (NRMSE), normalized mean absolute error (NMAE) and adjusted-r 2 . Cross-validation with 5-fold approach was conducted to test the generalizability of each model. Results:The fractional regression model with loglog as a link function consistently performed best in all country-specific value sets. For instance, the NRMSE (0.1282) and NMAE (0.0914) were the lowest, while adjusted-r 2 was the highest (52.5%) when the English value set was considered. Among D-39 dimensions, the energy and mobility was the only one that was consistently significant for all models. Conclusions:The D-39 can be mapped onto the EQ-5D-5L utilities with good predictive accuracy. The fractional regression model, which is appropriate for handling bounded outcomes, outperformed other candidate methods in all country-specific value sets. However, the regression coefficients differed reflecting preference heterogeneity across countries.

An Updated Systematic Review of Studies Mapping (or Cross-Walking) Measures of Health-Related Quality of Life to Generic Preference-Based Measures to Generate Utility Values

“…Some populations were mixed, for example, Chen, 2015 [62], Richardson, 2014 [63] used the Multi-instrument Comparison (MIC) dataset [253] which has all the generic PBMs and contains both patients (selfidentified) and members of the general population who were healthy. A number of studies relied on the MIC dataset to undertake mapping within specified patient populations including asthma [69,212], depression [64], diabetes [65,202], cancer [205] and heart disease [68].…”

Predicting EQ-5D Index Scores from the PROMIS-29 Profile for the United Kingdom, France, and Germany

“…Revicki used the EQ-5D-3L questionnaire and applied the US EQ-5D-3L value set by Shaw (2015) (25). EQ-5D index values and mappings are country-speci c (8,26). Revicki's model can therefore only be used to predict EQ-5D scores in the US.…”

Testing alternative regression models to predict utilities: mapping the QLQ-C30 onto the EQ-5D-5L and the SF-6D

“…We normalized both MAE and RMSE to the range of the observed data. Such normalization produce non-dimensional scale, and facilitate the comparison between datasets or models with different scales [30].…”

“…The MIC data is an ideal source for deriving mapping algorithms from disease-specific outcome measures onto generic preference-based measures. So far it has been applied to develop several mapping algorithms in different chronic diseases, including asthma [26] depression [27; 28], heart diseases [29] and diabetes [30].…”

“…The complementary loglog (cloglog) is the best alternative functional form in both EQ-5D-5L and SF-6D prediction, and is used as a link function. The FRM model has been detailed elsewhere [30]. The BB regression is a fully parametric counterpart, which is flexible and capable of modeling dependent variables restricted between 0 and 1 [39].…”