Mapping the Paediatric Quality of Life Inventory (PedsQL™) Generic Core Scales onto the Child Health Utility Index–9 Dimension (CHU-9D) Score for Economic Evaluation in Children

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@article{be2d89db30a3488eac65f8a09c5fc917,
title = "Mapping the Paediatric Quality of Life Inventory (PedsQL{\texttrademark}) Generic Core Scales onto the Child Health Utility Index–9 Dimension (CHU-9D) Score for Economic Evaluation in Children",
abstract = "Background The Paediatric Quality of Life Inventory (PedsQL{\texttrademark}) questionnaire is a widely used, generic instrument designed for measuring health-related quality of life (HRQoL); however, it is not preference-based and therefore not suitable for cost–utility analysis. The Child Health Utility Index–9 Dimension (CHU-9D), however, is a preference-based instrument that has been primarily developed to support cost–utility analysis. Objective This paper presents a method for estimating CHU-9D index scores from responses to the PedsQL{\texttrademark} using data from a randomised controlled trial of prednisolone therapy for treatment of childhood corticosteroid-sensitive nephrotic syndrome. Methods HRQoL data were collected from children at randomisation, week 16, and months 12, 18, 24, 36 and 48. Observations on children aged 5 years and older were pooled across all data collection timepoints and were then randomised into an estimation (n = 279) and validation (n = 284) sample. A number of models were developed using the estimation data before internal validation. The best model was chosen using multi-stage selection criteria. Results Most of the models developed accurately predicted the CHU-9D mean index score. The best performing model was a generalised linear model (mean absolute error = 0.0408; mean square error = 0.0035). The proportion of index scores deviating from the observed scores by <  0.03 was 53%. Conclusions The mapping algorithm provides an empirical tool for estimating CHU-9D index scores and for conducting cost–utility analyses within clinical studies that have only collected PedsQL{\texttrademark} data. It is valid for children aged 5 years or older. Caution should be exercised when using this with children younger than 5 years, older adolescents (>  13 years) or patient groups with particularly poor quality of li",
author = "Tosin Lambe and Emma Frew and Natalie Ives and Rebecca Woolley and Carole Cummins and Elizabeth Brettell and Emma Barsoum and Webb, {Nicholas J A}",
year = "2017",
month = dec,
day = "20",
doi = "10.1007/s40273-017-0600-7",
language = "English",
journal = "PharmacoEconomics",
issn = "1170-7690",
publisher = "Springer",

}

RIS

TY - JOUR

T1 - Mapping the Paediatric Quality of Life Inventory (PedsQL™) Generic Core Scales onto the Child Health Utility Index–9 Dimension (CHU-9D) Score for Economic Evaluation in Children

AU - Lambe, Tosin

AU - Frew, Emma

AU - Ives, Natalie

AU - Woolley, Rebecca

AU - Cummins, Carole

AU - Brettell, Elizabeth

AU - Barsoum, Emma

AU - Webb, Nicholas J A

PY - 2017/12/20

Y1 - 2017/12/20

N2 - Background The Paediatric Quality of Life Inventory (PedsQL™) questionnaire is a widely used, generic instrument designed for measuring health-related quality of life (HRQoL); however, it is not preference-based and therefore not suitable for cost–utility analysis. The Child Health Utility Index–9 Dimension (CHU-9D), however, is a preference-based instrument that has been primarily developed to support cost–utility analysis. Objective This paper presents a method for estimating CHU-9D index scores from responses to the PedsQL™ using data from a randomised controlled trial of prednisolone therapy for treatment of childhood corticosteroid-sensitive nephrotic syndrome. Methods HRQoL data were collected from children at randomisation, week 16, and months 12, 18, 24, 36 and 48. Observations on children aged 5 years and older were pooled across all data collection timepoints and were then randomised into an estimation (n = 279) and validation (n = 284) sample. A number of models were developed using the estimation data before internal validation. The best model was chosen using multi-stage selection criteria. Results Most of the models developed accurately predicted the CHU-9D mean index score. The best performing model was a generalised linear model (mean absolute error = 0.0408; mean square error = 0.0035). The proportion of index scores deviating from the observed scores by <  0.03 was 53%. Conclusions The mapping algorithm provides an empirical tool for estimating CHU-9D index scores and for conducting cost–utility analyses within clinical studies that have only collected PedsQL™ data. It is valid for children aged 5 years or older. Caution should be exercised when using this with children younger than 5 years, older adolescents (>  13 years) or patient groups with particularly poor quality of li

AB - Background The Paediatric Quality of Life Inventory (PedsQL™) questionnaire is a widely used, generic instrument designed for measuring health-related quality of life (HRQoL); however, it is not preference-based and therefore not suitable for cost–utility analysis. The Child Health Utility Index–9 Dimension (CHU-9D), however, is a preference-based instrument that has been primarily developed to support cost–utility analysis. Objective This paper presents a method for estimating CHU-9D index scores from responses to the PedsQL™ using data from a randomised controlled trial of prednisolone therapy for treatment of childhood corticosteroid-sensitive nephrotic syndrome. Methods HRQoL data were collected from children at randomisation, week 16, and months 12, 18, 24, 36 and 48. Observations on children aged 5 years and older were pooled across all data collection timepoints and were then randomised into an estimation (n = 279) and validation (n = 284) sample. A number of models were developed using the estimation data before internal validation. The best model was chosen using multi-stage selection criteria. Results Most of the models developed accurately predicted the CHU-9D mean index score. The best performing model was a generalised linear model (mean absolute error = 0.0408; mean square error = 0.0035). The proportion of index scores deviating from the observed scores by <  0.03 was 53%. Conclusions The mapping algorithm provides an empirical tool for estimating CHU-9D index scores and for conducting cost–utility analyses within clinical studies that have only collected PedsQL™ data. It is valid for children aged 5 years or older. Caution should be exercised when using this with children younger than 5 years, older adolescents (>  13 years) or patient groups with particularly poor quality of li

U2 - 10.1007/s40273-017-0600-7

DO - 10.1007/s40273-017-0600-7

M3 - Article

JO - PharmacoEconomics

JF - PharmacoEconomics

SN - 1170-7690

ER -