Evidence map›Paper›PMID 31338698›Full record

ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2019

Mapping the kidney disease quality of life 36-item short form survey (KDQOL-36) to the EQ-5D-3L and the EQ-5D-5L in patients undergoing dialysis.

Fan Yang, Carlos K H Wong, Nan Luo, James Piercy, Rebecca Moon, James Jackson

Abstract read
In one paragraph

Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 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

25 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. 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
  5. Review
  6. Article
  7. Article
  8. Article
  9. 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
  10. Article
  11. Article
  12. Article
  13. Article
  14. Mapping the Peds QLFrontiers in public health · 2023
    Article
  15. Article
  16. Article
  17. Mapping the Haem-A-QoL to the EQ-5D-5L in patients with hemophilia.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2022
    Article
  18. Review
  19. An initial evaluation of expanded hemodialysis on hospitalizations, drug utilization, costs, and patient utility in Colombia.Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy · 2021
    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

6 authors.

Fan YangCentre for Health Economics, University of York, York, UK. fan.bella.yang@york.ac.uk.ORCID http://orcid.org/0000-0003-4689-265X
Carlos K H WongDepartment of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.
Nan LuoSaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
James PiercyAdelphi Real World, Bollington, UK.
Rebecca MoonAdelphi Real World, Bollington, UK.
James JacksonAdelphi Real World, Bollington, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo develop algorithms mapping the Kidney Disease Quality of Life 36-Item Short Form Survey (KDQOL-36) onto the 3-level EQ-5D questionnaire (EQ-5D-3L) and the 5-level EQ-5D questionnaire (EQ-5D-5L) for patients with end-stage renal disease requiring dialysis.

methodsWe used data from a cross-sectional study in Europe (France, n = 299; Germany, n = 413; Italy, n = 278; Spain, n = 225) to map onto EQ-5D-3L and data from a cross-sectional study in Singapore (n = 163) to map onto EQ-5D-5L. Direct mapping using linear regression, mixture beta regression and adjusted limited dependent variable mixture models (ALDVMMs) and response mapping using seemingly unrelated ordered probit models were performed. The KDQOL-36 subscale scores, i.e., physical component summary (PCS), mental component summary (MCS), three disease-specific subscales or their average, i.e., kidney disease component summary (KDCS), and age and sex were included as the explanatory variables. Predictive performance was assessed by mean absolute error (MAE) and root mean square error (RMSE) using 10-fold cross-validation.

resultsMixture models outperformed linear regression and response mapping. When mapping to EQ-5D-3L, the ALDVMM model was the best-performing one for France, Germany and Spain while beta regression was best for Italy. When mapping to EQ-5D-5L, the ALDVMM model also demonstrated the best predictive performance. Generally, models using KDQOL-36 subscale scores showed better fit than using the KDCS.

conclusionsThis study adds to the growing literature suggesting the better performance of the mixture models in modelling EQ-5D and produces algorithms to map the KDQOL-36 onto EQ-5D-3L (for France, Germany, Italy, and Spain) and EQ-5D-5L (for Singapore).

Indexed as

AlgorithmsHealth Status IndicatorsQuality of LifeAdultAgedCost-Benefit AnalysisCross-Sectional StudiesEuropeFemaleHumansKidney Failure, ChronicLinear ModelsMaleMiddle AgedRenal DialysisSingaporeDialysisEQ-5D-3LEQ-5D-5LKDQOL-36Mapping

Identifiers

PMID31338698
PMCPMC6803593

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.