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Mapping the PedsQL™ onto the CHU9D: An Assessment of External Validity in a Large Community-Based Sample
Christine Mpundu-Kaambwa1, Gang Chen2, Elisabeth Huynh3
1Institute for Choice, University of South Australia Business School, Level 3 Way Lee Building, North Terrace, Adelaide, SA, 5001, Australia. christine.mpundu-kaambwa@unisa.edu.au.
Pharmacoeconomics
|June 5, 2019
Summary
Mapping algorithms predict health utilities but require external validation. This study found that while generally accurate, the best algorithm depends on the specific child population, especially those with health conditions.
Area of Science:
- Health Economics
- Psychometrics
- Pediatric Health Outcomes
Background:
- Mapping algorithms estimate health utilities for cost-utility analysis when direct preference-based measures are absent.
- Assessing the external validity of these algorithms is crucial due to potentially variable predictive performance.
Purpose of the Study:
- To evaluate the external validity and generalizability of current mapping algorithms.
- To predict Child Health Utility 9D (CHU9D) utilities from Pediatric Quality of Life Inventory (PedsQL) scores in children and adolescents.
- To assess performance in populations with and without disabilities or health conditions.
Main Methods:
- Externally validated five existing mapping algorithms using data from the Longitudinal Study of Australian Children (n=6623).
- Assessed predictive accuracy using Mean Absolute Error (MAE) and Mean Squared Error (MSE).
- Compared algorithm performance across different demographic and health status groups.
Main Results:
- All validated mapping algorithms demonstrated acceptable predictive accuracy within published ranges (MAE: 0.0741-0.2302).
- Algorithms performed less accurately for children and adolescents with disabilities/health conditions compared to those without.
- Performance varied between Australian and UK-derived algorithms and across different populations.
Conclusions:
- Published mapping algorithms offer acceptable predictive accuracy for health utilities.
- The optimal mapping algorithm choice is population-dependent, particularly for pediatric populations with health conditions.
- External validation is essential for reliable application of mapping algorithms in cost-utility analyses.
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