Evidence map›Paper›PMID 25169558›Full record

ArticleHealth and quality of life outcomes2014

From KIDSCREEN-10 to CHU9D: creating a unique mapping algorithm for application in economic evaluation.

Gang Chen, Katherine Stevens, Donna Rowen, Julie Ratcliffe

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Article in Health and quality of life outcomes, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.

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37citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

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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

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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, 1 synthesis or guideline pooled it.

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  14. Development of algorithms for estimating the Child Health Utility 9D from Caregiver Priorities and Child Health Index of Life with Disability.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024
    Article
  15. 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
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  19. A Cluster Randomized Controlled Trial of theChildren (Basel, Switzerland) · 2023
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Gang ChenFlinders Health Economics Group, School of Medicine, Flinders University, Adelaide, Australia. gang.chen@flinders.edu.au.
Katherine Stevens
Donna Rowen
Julie Ratcliffe

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe KIDSCREEN-10 index and the Child Health Utility 9D (CHU9D) are two recently developed generic instruments for the measurement of health-related quality of life in children and adolescents. Whilst the CHU9D is a preference based instrument developed specifically for application in cost-utility analyses, the KIDSCREEN-10 is not currently suitable for application in this context. This paper provides an algorithm for mapping the KIDSCREEN-10 index onto the CHU9D utility scores.

methodsA sample of 590 Australian adolescents (aged 11-17) completed both the KIDSCREEN-10 and the CHU9D. Several econometric models were estimated, including ordinary least squares estimator, censored least absolute deviations estimator, robust MM-estimator and generalised linear model, using a range of explanatory variables with KIDSCREEN-10 items scores as key predictors. The predictive performance of each model was judged using mean absolute error (MAE) and root mean squared error (RMSE).

resultsThe MM-estimator with stepwise-selected KIDSCREEN-10 items scores as explanatory variables had the best predictive accuracy using MAE, whilst the equivalent ordinary least squares model had the best predictive accuracy using RMSE.

conclusionsThe preferred mapping algorithm (i.e. the MM-estimate with stepwise selected KIDSCREEN-10 item scores as the predictors) can be used to predict CHU9D utility from KIDSCREEN-10 index with a high degree of accuracy. The algorithm may be usefully applied within cost-utility analyses to generate cost per quality adjusted life year estimates where KIDSCREEN-10 data only are available.

Indexed as

AlgorithmsCost-Benefit AnalysisCost of IllnessHealth Status IndicatorsQuality of LifeAdolescentAustraliaChildChild WelfareFemaleHealth SurveysHumansLinear ModelsMaleModels, EconomicQuality-Adjusted Life Years

Identifiers

PMID25169558
PMCPMC4243726

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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.