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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

Overview of attention for article published in Quality of Life Research, March 2018
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  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

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2 tweeters
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1 Facebook page

Citations

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2 Dimensions

Readers on

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10 Mendeley
Title
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
Published in
Quality of Life Research, March 2018
DOI 10.1007/s11136-018-1840-5
Pubmed ID
Authors

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

Abstract

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. 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. 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 20%
Professor 1 10%
Student > Doctoral Student 1 10%
Student > Ph. D. Student 1 10%
Unspecified 1 10%
Other 1 10%
Unknown 3 30%
Readers by discipline Count As %
Unspecified 1 10%
Nursing and Health Professions 1 10%
Business, Management and Accounting 1 10%
Psychology 1 10%
Economics, Econometrics and Finance 1 10%
Other 0 0%
Unknown 5 50%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 August 2018.
All research outputs
#8,095,991
of 14,158,129 outputs
Outputs from Quality of Life Research
#930
of 2,123 outputs
Outputs of similar age
#141,715
of 277,487 outputs
Outputs of similar age from Quality of Life Research
#29
of 72 outputs
Altmetric has tracked 14,158,129 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,123 research outputs from this source. They receive a mean Attention Score of 4.0. This one has gotten more attention than average, scoring higher than 53% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 277,487 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 72 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.