Evidence map›Paper›PMID 29569014›Full record

SynthesisQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation2018

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.

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

Open access · greenAbstract readMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
8.7field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.

  1. Pooled it
  2. HIT-6 and EQ-5D-5L in patients with migraine: assessment of common latent constructs and development of a mapping algorithm.The European journal of health economics : HEPAC : health economics in prevention and care · 2022
    Trial
  3. Mapping the Problem Areas in Diabetes (PAID) and Diabetes Distress Scale (DDS) to the EQ-5D-5L in diabetes care.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026
    Article
  4. Article
  5. Development of algorithms to estimate the EQ-5D-5L from the FACT-L in patients with lung cancer: a mapping study.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024
    Article
  6. Mapping the 12-item World Health Organization disability assessment schedule 2.0 (WHODAS 2.0) onto the assessment of quality of life (AQoL)-4D utilities.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024
    Article
  7. Article
  8. Article
  9. Article
  10. Validation of a reduced instrument Diabetes-21 for assessing health-related quality of life among people with diabetes.Epidemiologia e servicos de saude : revista do Sistema Unico de Saude do Brasil · 2022
    Article
  11. Article
  12. Exploring health-related quality of life and frailty in older adults based on the Korean Frailty and Aging Cohort Study.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2020
    Article
  13. Does linear equating improve prediction in mapping? Crosswalking MacNew onto EQ-5D-5L value sets.The European journal of health economics : HEPAC : health economics in prevention and care · 2020
    Article
  14. Article
  15. Testing alternative regression models to predict utilities: mapping the QLQ-C30 onto the EQ-5D-5L and the SF-6D.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2018
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Admassu N LamuDepartment of Community Medicine, University of Tromsø, Tromsø, Norway.
Gang ChenCentre for Health Economics, Monash Business School, Monash University, Melbourne, Australia. gang.chen@monash.edu.ORCID 0000-0002-8385-5965
Thor Gamst-KlaussenDepartment of Community Medicine, University of Tromsø, Tromsø, Norway.
Jan Abel OlsenDepartment of Community Medicine, University of Tromsø, Tromsø, Norway.
UiT The Arctic University of Norway · NOMonash Health · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo 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.

methodsData 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-r

resultsThe 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

conclusionsThe 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.

Indexed as

AlgorithmsPatient Reported Outcome MeasuresDiabetes MellitusFemaleHumansMaleResearch DesignSickness Impact ProfileSurveys and QuestionnairesDiabetes-39EQ-5D-5LHRQoLMappingQALYUtility

Identifiers

PMID29569014
OpenAlexW2791402797

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.