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Quality of Life Research
Gepubliceerd in: Quality of Life Research 3/2020

19-11-2019

Mapping PedsQLTM scores onto CHU9D utility scores: estimation, validation and a comparison of alternative instrument versions

Auteurs: Rohan Sweeney, Gang Chen, Lisa Gold, Fiona Mensah, Melissa Wake

Gepubliceerd in: Quality of Life Research | Uitgave 3/2020

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Abstract

Background

The Paediatric Quality of Life InventoryTM 4.0 Generic Core Scales (PedsQL) is a non-preference based instrument for assessing health related quality of life (HRQoL) in children. Recent papers presented algorithms of parental proxy and short-form versions of the PedsQL onto the validated preference-based Child Health Utility 9D (CHU9D) instrument, to enable conversion of PedsQL scores to quality adjusted life years for use in economic evaluation. However, further research was needed to both validate these algorithms, and assess if use of the full 23-item PedsQL self-report instrument is preferable to other PedsQL versions for mapping onto child self-report CHU9D utilities.

Objective

To develop a mapping algorithm for converting the 23-item PedsQL instrument onto the CHU9D instrument and provide an external validation of two recently published algorithms that might be considered alternatives.

Methods

Data from children in the Longitudinal Study of Australian Children (LSAC) were used (N = 1801). Six econometric methods were compared to identify the best algorithms, assessed against a series of goodness-of-fit criteria. The same data and goodness-of-fit criteria were used in the external validation exercise for previously published mapping algorithms.

Results

The optimal mapping algorithm was identified, which used PedsQL dimension scores to predict the CHU9D utilities. It performed well against standard goodness-of-fit tests. The external validation exercise revealed the recently published alternative algorithms also performed relatively well.

Conclusion

The identified mapping algorithms can be used to facilitate cost-utility analysis in comparable populations when only the PedsQL instrument is available. Results from this population indicate the algorithms identified in this paper are well suited for estimating CHU9D self-report utilities when the full 23-item self-report PedsQL instrument has been used.
vorige artikel The cost-effectiveness analysis of drug therapy versus surgery for symptomatic adenoid hypertrophy by a Markov model
volgende artikel Correction to: Mapping PedsQL™ scores onto CHU9D utility scores: estimation, validation and a comparison of alternative instrument versions
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Voetnoten
1
Whilst the need for a representative Australian sample is not applicable in this analysis, survey weights are available for research investigations that do have that requirement.
 
2
The 23 item PedsQL instrument enables calculation of overall and dimension-level scores even when some individual questions/items have not been answered (see https://​www.​pedsql.​org/​score.​html).
 
3
Note that we initially also considered the beta mixture regression proposed by Gray and Hernández-Alava [23]; however, the single class beta regression was found to perform better than latent class regressions for each model-level, as such the beta mixture regression results were omitted. The multivariable fractional polynomials (MFP) regression approach (which was estimated using a robust regression model) was also initially investigated for inclusion as a seventh econometric estimation method. However, it was ruled out for inclusion as it was found to be unstable in Model 2 validations, producing a lower bound estimate of − 0.4825 CHU9D utility score in the validation exercise (further MFP results are available upon request).
 
4
Overall 26.2% children reported any medical condition. The most commonly reported condition was asthma (reported by 13.5% children); others include allergy, blood clotting condition, bone disorder, broken bone(s) that are still healing, chronic or recurring pain/discomfort, diabetes, difficulty gripping or holding things, hearing loss, incomplete use of arms/fingers/feet/legs, infection or injury to toes/toenails, mental illnesses, restriction in physical activities, shortness of breath causing restriction and vision loss.
 
Literatuur
1.
Brazier, J., Ratcliffe, J., Tsuchiya, A., & Salomon, J. (2007). Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press.
2.
3.
Commonwealth of Australia. (2016). Guidelines for preparing submissions to the Pharmaceutical Benefits Advisory Committee (Version 5.0). Canberra.
4.
5.
Wolstenholme, J. L., Bargo, D., Wang, K., Harnden, A., Räisänen, U., & Abel, L. (2018). Preference-based measures to obtain health state utility values for use in economic evaluations with child-based populations: A review and UK-based focus group assessment of patient and parent choices. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation.,27(7), 1769–1780.CrossRef
6.
Chen, G., & Ratcliffe, J. (2015). A review of the development and application of generic multi-attribute utility instruments for paediatric populations. PharmacoEconomics,33(10), 1013–1028.CrossRef
7.
Wailoo, A. J., Hernandez-Alava, M., Manca, A., Mejia, A., Ray, J., Crawford, B., et al. (2017). Mapping to estimate health-state utility from non-preference-based outcome measures: An ISPOR good practices for outcomes research task force report. Value Health,20(1), 18–27.CrossRef
8.
Varni, J. W., Seid, M., & Kurtin, P. S. (2001). PedsQL 4.0: Reliability and validity of the Pediatric Quality of Life Inventory version 4.0 generic core scales in healthy and patient populations. Medical Care,39(8), 800–812.CrossRef
9.
Mpundu-Kaambwa, C., Chen, G., Russo, R., Stevens, K., Petersen, K. D., & Ratcliffe, J. (2017). Mapping CHU9D utility scores from the PedsQLTM 4.0 SF-15. Pharmacoeconomics,35(4), 453–467.CrossRef
10.
Khan, K. A., Petrou, S., Rivero-Arias, O., Walters, S. J., & Boyle, S. E. (2014). Mapping EQ-5D utility scores from the PedsQL generic core scales. Pharmacoeconomics,32(7), 693–706.CrossRef
11.
Stevens, K. (2012). Valuation of the Child Health Utility 9D Index. Pharmacoeconomics,30(8), 729–747.CrossRef
12.
Stevens, K. J. (2010). Working with children to develop dimensions for a preference-based, generic, pediatric, health-related quality-of-life measure. Qualitative Health Research,20(3), 340–351.CrossRef
13.
Payakachat, N., Tilford, J. M., Kuhlthau, K. A., van Exel, N. J., Kovacs, E., Bellando, J., et al. (2014). Predicting health utilities for children with autism spectrum disorders. Autism Research,7(6), 649–663.CrossRef
14.
Lambe, T., Frew, E., Ives, N. J., Woolley, R. L., Cummins, C., Brettell, E. A., et al. (2018). 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. PharmacoEconomics,36(4), 451–465.CrossRef
15.
16.
Clifford, S. A., Davies, S., & Wake, M. (2019). Child Health CheckPoint: Cohort summary and methodology of a physical health and biospecimen module for the Longitudinal Study of Australian Children. British Medical Journal Open,9(Suppl 3), 3–22.
17.
Stevens, K. (2011). Assessing the performance of a new generic measure of health-related quality of life for children and refining it for use in health state valuation. Applied Health Economics and Health Policy,9(3), 157–169.CrossRef
18.
Stevens, K., & Ratcliffe, J. (2012). Measuring and valuing health benefits for economic evaluation in adolescence: An assessment of the practicality and validity of the Child Health Utility 9D in the Australian adolescent population. Value in Health,15(8), 1092–1099.CrossRef
19.
Ratcliffe, J., Stevens, K., Flynn, T., Brazier, J., & Sawyer, M. (2012). An assessment of the construct validity of the CHU9D in the Australian adolescent general population. Quality of Life Research,21(4), 717–725.CrossRef
20.
Chen, G., Flynn, T., Stevens, K., Brazier, J., Huynh, E., Sawyer, M., et al. (2015). Assessing the health-related quality of life of Australian adolescents: An empirical comparison of the Child Health Utility 9D and EQ-5D-Y instruments. Value in Health,18(4), 432–438.CrossRef
21.
Petersen, K. D., Chen, G., Mpundu-Kaambwa, C., Stevens, K., Brazier, J., & Ratcliffe, J. (2018). Measuring health-related quality of life in adolescent populations: An empirical comparison of the CHU9D and the PedsQLTM 4.0 Short Form 15. The Patient-Patient-Centered Outcomes Research,11(1), 29–37.CrossRef
22.
Ratcliffe, J., Huynh, E., Chen, G., Stevens, K., Swait, J., Brazier, J., et al. (2016). Valuing the Child Health Utility 9D: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm. Social Science and Medicine,157, 48–59.CrossRef
23.
Gray, L., & Hernández-Alava, M. (2018). BETAMIX: A command for fitting mixture regression models for bounded dependent variables using the beta distribution. The Stata Journal,18(1), 51–75.CrossRef
24.
Brazier, J. E., Yang, Y., Tsuchiya, A., & Rowen, D. L. (2010). A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. The European Journal of Health Economics,11(2), 215–225.CrossRef
25.
Australian Bureau of Statistics. (2011). Census of population and housing: Socio-Economic Indexes for Areas (SEIFA) Australia 2011 (cat. no. 2033.0.55.001).
Metagegevens
Titel
Mapping PedsQLTM scores onto CHU9D utility scores: estimation, validation and a comparison of alternative instrument versions
Auteurs
Rohan Sweeney
Gang Chen
Lisa Gold
Fiona Mensah
Melissa Wake
Publicatiedatum
19-11-2019
Uitgeverij
Springer International Publishing
Gepubliceerd in
Quality of Life Research / Uitgave 3/2020
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-019-02357-9

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