Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/127323
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Type: Journal article
Title: Mapping the PedsQL™ onto the CHU9D: an assessment of external validity in a large community-based sample
Other Titles: Mapping the PedsQL (TM) onto the CHU9D: an assessment of external validity in a large community-based sample
Author: Mpundu-Kaambwa, C.
Chen, G.
Huynh, E.
Russo, R.
Ratcliffe, J.
Citation: PharmacoEconomics, 2019; 37(9):1139-1153
Publisher: Springer Nature
Issue Date: 2019
ISSN: 1170-7690
1179-2027
Statement of
Responsibility: 
Christine Mpundu‑Kaambwa, Gang Chen, Elisabeth Huynh, Remo Russo, Julie Ratcliffe
Abstract: Background: Mapping algorithms have been indicated as a second-best solution for estimating health state utilities for the calculation of quality-adjusted life-years within cost-utility analysis when no generic preference-based measure is incorporated into the study. However, the predictive performance of these algorithms may be variable and hence it is important to assess their external validity before application in different settings. Objective: The aim of this study was to assess the external validity and generalisability of existing mapping algorithms for predicting preference-based Child Health Utility 9D (CHU9D) utilities from non-preference-based Pediatric Quality of Life Inventory (PedsQL) scores among children and adolescents living with or without disabilities or health conditions. Methods: Five existing mapping algorithms, three developed using data from an Australian community population and two using data from a UK population with one or more self-reported health conditions, were externally validated on data from the Longitudinal Study of Australian Children (n = 6623). The predictive accuracy of each mapping algorithm was assessed using the mean absolute error (MAE) and the mean squared error (MSE). Results: Values for the MAE (0.0741-0.2302) for all validations were within the range of published estimates. In general, across all ages, the algorithms amongst children and adolescents with disabilities/health conditions (Australia MAE: 0.2085-0.2302; UK MAE: 0.0854-0.1162) performed worse relative to those amongst children and adolescents without disabilities/health conditions (Australia MAE: 0.1424-0.1645; UK MAE: 0.0741-0.0931). Conclusions: The published mapping algorithms have acceptable predictive accuracy as measured by MAE and MSE. The findings of this study indicate that the choice of the most appropriate mapping algorithm to apply may vary according to the population under consideration.
Keywords: Humans
Health Status Indicators
Reproducibility of Results
Health Status
Algorithms
Quality-Adjusted Life Years
Quality of Life
Adolescent
Child
Disabled Children
Cost-Benefit Analysis
Australia
Female
Male
Self Report
Surveys and Questionnaires
Child Health
United Kingdom
Rights: © Springer Nature Switzerland AG 2019
DOI: 10.1007/s40273-019-00808-2
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