Evidence map›Paper›PMID 29909881›Full record

ArticleValue in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research2018

Development of Methods for the Mapping of Utilities Using Mixture Models: Mapping the AQLQ-S to the EQ-5D-5L and the HUI3 in Patients with Asthma.

Laura A Gray, Mónica Hernández Alava, Allan J Wailoo

Abstract read
In one paragraph

Article in Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

37 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Mapping the modified rankin scale (mRS) onto the assessment of quality of life (AQoL) utilities.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025
    Trial
  4. Trial
  5. Estimating the impact of changes in weight and BMI on EQ-5D-3L: a longitudinal analysis of a behavioural group-based weight loss intervention.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2022
    Trial
  6. 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
  7. Mapping of the acromegaly quality of life questionnaire to ED-5D-5L index score among patients with acromegaly.The European journal of health economics : HEPAC : health economics in prevention and care · 2021
    Trial
  8. Article
  9. Article
  10. Predicting EQ-5D-3L utility values from clinical data in a prospective cohort of kidney transplant recipients.The European journal of health economics : HEPAC : health economics in prevention and care · 2026
    Observational
  11. Development of mapping algorithms for gastric cancer: translating EORTC QLQ-C30 and QLQ-STO22 to EQ-5D-5L health utilities.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025
    Article
  12. Article
  13. Article
  14. Article
  15. 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
  16. Mapping the EORTC QLQ-C30 and QLQ H&N35 to the EQ-5D-5L and SF-6D for papillary thyroid carcinoma.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024
    Article
  17. Article
  18. Article
  19. Enabling QALY estimation in mental health trials and care settings: mapping from the PHQ-9 and GAD-7 to the ReQoL-UI or EQ-5D-5L using mixture models.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2023
    Article
  20. 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

3 authors.

Laura A GraySchool of Health and Related Research, University of Sheffield, Sheffield, UK.
Mónica Hernández AlavaSchool of Health and Related Research, University of Sheffield, Sheffield, UK. Electronic address: monica.hernandez@sheffield.ac.uk.
Allan J WailooSchool of Health and Related Research, University of Sheffield, Sheffield, UK.

Funding

Medical Research Council MR/L022575/1
6 · The paper itself

Abstract

backgroundStudies have shown that methods based on mixture models work well when mapping clinical to preference-based methods.

objectivesTo develop these methods in different ways and to compare performance in a case study.

methodsData from 856 patients with asthma allowed mapping between the Asthma Quality of Life Questionnaire and both the five-level EuroQol five-dimensional questionnaire (EQ-5D-5L) and the health utilities index mark 3 (HUI3). Adjusted limited dependent variable mixture models and beta-based mixture models were estimated. Optional inclusion of the gap between full health and the next value as well as a mass point at the next feasible value were explored.

resultsIn all cases, model specifications formally modeling the gap between full health and the next feasible value were an improvement on those that did not. Mapping to the HUI3 required more components in the mixture models than did mapping to the EQ-5D-5L because of its uneven distribution. The optimal beta-based mixture models mapping to the HUI3 included a probability mass at the utility value adjacent to full health. This is not the case when estimating the EQ-5D-5L, because of the low proportion of observations at this point.

conclusionsBeta-based mixture models marginally outperformed adjusted limited dependent variable mixture models with the same number of components in this data set. Nevertheless, they require a larger number of parameters and longer estimation time. Both mixture model types closely fit both EQ-5D-5L and HUI data. Standard mapping approaches typically lead to biased estimates of health gain. The mixture model approaches exhibit no such bias. Both can be used with confidence in applied cost-effectiveness studies. Future mapping studies in other disease areas should consider similar methods.

Indexed as

AdolescentAdultAgedAged, 80 and overAlgorithmsAnti-Asthmatic AgentsAsthmaFemaleHealth StatusHumansMaleMiddle AgedModels, StatisticalPatient PreferenceProbabilityQuality-Adjusted Life YearsAnti-Asthmatic AgentsEQ-5DHUImappingutility

Identifiers

PMID29909881
PMCPMC6026598

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LicenceCC BY
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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.