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Quality of Life Research

Erschienen in:

22.03.2018

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

verfasst von: Admassu N. Lamu, Gang Chen, Thor Gamst-Klaussen, Jan Abel Olsen

Erschienen in: Quality of Life Research | Ausgabe 7/2018

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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, generalised 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 on normalised root mean square error (NRMSE), normalised mean absolute error (NMAE) and adjusted-r². Cross-validation with fivefold 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-r² 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.

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For example, the transformed score for energy and mobility (EM) domain is calculated by subtracting the sum of reverse-coded responses across all 15 items (say, X) from its minimum score (15) and divided by the range (maximum (105) minus minimum scores). That is, the algorithm for computing the EM score is: (X-15)/(105-15).

MM estimation estimates the regression parameter using S estimation, which minimises the scale of the residual from M estimation and then proceed with M estimation. The S in ‘S estimation’ stands for ‘scale’ of the residual, M in ‘M estimation’ for ‘maximum likelihood type’ and MM in ‘MM estimation’ stands for ‘minimising M estimation’. For detail, see Yohai [37].

RMSE and MAE were adjusted for scale differences, and normalised RMSE and normalised MAE were reported in the present study.

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Titel: 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
verfasst von: Admassu N. Lamu
Gang Chen
Thor Gamst-Klaussen
Jan Abel Olsen
Publikationsdatum: 22.03.2018
Verlag: Springer International Publishing
Erschienen in: Quality of Life Research / Ausgabe 7/2018
Print ISSN: 0962-9343
Elektronische ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-018-1840-5

Springer Medizin

Abstract

Purpose

Methods

Results

Conclusions

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