Evidence map›Paper›PMID 27072351›Full record

ArticleHealth and quality of life outcomes2016

Comparing the mapping between EQ-5D-5L, EQ-5D-3L and the EORTC-QLQ-C30 in non-small cell lung cancer patients.

Iftekhar Khan, Steve Morris, Nora Pashayan, Bashir Matata, Zahid Bashir, Joe Maguirre

Abstract readComparative Study
In one paragraph

Article in Health and quality of life outcomes, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 4 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

39 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Trial
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. 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
  19. 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
  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.

Iftekhar KhanClinical Trials Unit & Department of Health Economics, University of Surrey, Guilford, UK. I.Khan@surrey.ac.uk.
Steve MorrisDepartment of Applied Health Research, University College London, London, UK.
Nora PashayanDepartment of Applied Health Research, University College London, London, UK.
Bashir MatataLiverpool Heart and Chest Hospital, Liverpool, UK.
Zahid BashirUniversity of Birmingham, Cancer School, Birmingham, UK.
Joe MaguirreLiverpool Heart and Chest Hospital, Liverpool, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSeveral mapping algorithms have been published with the EORTC-QLQ-C30 for estimating EQ-5D-3L utilities. However, none are available with EQ-5D-5L. Moreover, a comparison between mapping algorithms in the same set of patients has not been performed for these two instruments simultaneously. In this prospective data set of 100 non-small cell lung cancer (NSCLC) patients, we investigate three mapping algorithms using the EQ-5D-3L and EQ-5D-5L and compare their performance.

methodsA prospective non-interventional cohort of 100 NSCLC patients were followed up for 12 months. EQ-5D-3L, EQ-5D-5L and EORTC-QLQ-C30 were assessed monthly. EQ-5D-5L was completed at least 1 week after EQ-5D-3L. A random effects linear regression model, a beta-binomial (BB) and a Limited Variable Dependent Mixture (LVDM) model were used to determine a mapping algorithm between EQ-5D-3L, EQ-5D-5L and QLQ-C30. Simulation and cross validation and other statistical measures were used to compare the performances of the algorithms.

resultsMapping from the EQ-5D-5L was better: lower AIC, RMSE, MAE and higher R(2) were reported with the EQ-5D-5L than with EQ-5D-3L regardless of the functional form of the algorithm. The BB model proved to be more useful for both instruments: for the EQ-5D-5L, AIC was -485, R(2) of 75 %, MAE of 0.075 and RMSE was 0.092. This was -385, 69 %, 0.099 and 0.113 for EQ-5D-3L respectively. The mean observed vs. predicted utilities were 0.572 vs. 0.577 and 0.515 vs. 0.523 for EQ-5D-5L and EQ-5D-3L respectively, for OLS; for BB, these were 0.572 vs. 0.575 and 0.515 vs. 0.518 respectively and for LVDMM 0.532 vs 0.515 and 0.569 vs 0.572 respectively. Less over-prediction at poorer health states was observed with EQ-5D-5L.

conclusionsThe BB mapping algorithm is confirmed to offer a better fit for both EQ-5D-3L and EQ-5D-5L. The results confirm previous and more recent results on the use of BB type modelling approaches for mapping. It is recommended that in studies where EQ-5D utilities have not been collected, an EQ-5D-5L mapping algorithm is used.

Indexed as

AlgorithmsAdultAgedAged, 80 and overCarcinoma, Non-Small-Cell LungCohort StudiesFemaleFollow-Up StudiesHealth StatusHumansLinear ModelsMaleMiddle AgedModels, TheoreticalProspective StudiesQuality of Life

Identifiers

PMID27072351
PMCPMC4830017

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.