Abstract
Methods: We used a large sample of children (n = 6898), with negligible missing data, from the Longitudinal Study of Australian Children. Exploratory factor analysis (EFA) and Spearman’s rank correlation coefficients were used to assess conceptual overlap between SDQ and CHU9D. Direct mapping (involving seven regression methods) and response mapping (involving one regression method) approaches were considered. The final model was selected by ranking the performance of each method by averaging the following across tenfold cross-validation iterations: mean absolute error (MAE), mean squared error (MSE), and MAE and MSE for two subsamples where predicted utility values were < 0.50 (poor health) or > 0.90 (healthy). External validation was conducted using data from the Child and Adolescent Mental Health Services study.
Results: SDQ and CHU9D were moderately correlated (ρ = − 0.52, p < 0.001). EFA demonstrated that all CHU9D domains were associated with four SDQ subscales. The best-performing model was the Generalized Linear Model with SDQ items and gender as predictors (full sample MAE: 0.1149; MSE: 0.0227). The new algorithm performed well in the external validation.
Conclusions: The proposed mapping algorithm can produce robust estimates of CHU9D utilities from SDQ data for economic evaluations. Further research is warranted to assess the applicability of the algorithm among children with severe health problems.
| Language | English |
|---|---|
| Pages | 2429-2441 |
| Number of pages | 13 |
| Journal | Quality of Life Research |
| Volume | 28 |
| Issue number | 9 |
| Early online date | 1 Jun 2019 |
| DOIs | |
| Publication status | Published - Sep 2019 |
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Keywords
- CHU9D
- Mapping
- SDQ
- Utility
Cite this
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Mapping the Strengths and Difficulties Questionnaire onto the Child Health Utility 9D in a large study of children. / Sharma, Rajan; Gu, Yuanyuan; Sinha, Kompal; Aghdaee, Mona; Parkinson, Bonny.
In: Quality of Life Research, Vol. 28, No. 9, 09.2019, p. 2429-2441.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Mapping the Strengths and Difficulties Questionnaire onto the Child Health Utility 9D in a large study of children
AU - Sharma, Rajan
AU - Gu, Yuanyuan
AU - Sinha, Kompal
AU - Aghdaee, Mona
AU - Parkinson, Bonny
PY - 2019/9
Y1 - 2019/9
N2 - Purpose: Non-preference-based measures cannot be used to directly obtain utilities but can be converted to preference-based measures through mapping. The only mapping algorithm for estimating Child Health Utility-9D (CHU9D) utilities from Strengths and Difficulties Questionnaire (SDQ) responses has limitations. This study aimed to develop a more accurate algorithm. Methods: We used a large sample of children (n = 6898), with negligible missing data, from the Longitudinal Study of Australian Children. Exploratory factor analysis (EFA) and Spearman’s rank correlation coefficients were used to assess conceptual overlap between SDQ and CHU9D. Direct mapping (involving seven regression methods) and response mapping (involving one regression method) approaches were considered. The final model was selected by ranking the performance of each method by averaging the following across tenfold cross-validation iterations: mean absolute error (MAE), mean squared error (MSE), and MAE and MSE for two subsamples where predicted utility values were < 0.50 (poor health) or > 0.90 (healthy). External validation was conducted using data from the Child and Adolescent Mental Health Services study.Results: SDQ and CHU9D were moderately correlated (ρ = − 0.52, p < 0.001). EFA demonstrated that all CHU9D domains were associated with four SDQ subscales. The best-performing model was the Generalized Linear Model with SDQ items and gender as predictors (full sample MAE: 0.1149; MSE: 0.0227). The new algorithm performed well in the external validation.Conclusions: The proposed mapping algorithm can produce robust estimates of CHU9D utilities from SDQ data for economic evaluations. Further research is warranted to assess the applicability of the algorithm among children with severe health problems.
AB - Purpose: Non-preference-based measures cannot be used to directly obtain utilities but can be converted to preference-based measures through mapping. The only mapping algorithm for estimating Child Health Utility-9D (CHU9D) utilities from Strengths and Difficulties Questionnaire (SDQ) responses has limitations. This study aimed to develop a more accurate algorithm. Methods: We used a large sample of children (n = 6898), with negligible missing data, from the Longitudinal Study of Australian Children. Exploratory factor analysis (EFA) and Spearman’s rank correlation coefficients were used to assess conceptual overlap between SDQ and CHU9D. Direct mapping (involving seven regression methods) and response mapping (involving one regression method) approaches were considered. The final model was selected by ranking the performance of each method by averaging the following across tenfold cross-validation iterations: mean absolute error (MAE), mean squared error (MSE), and MAE and MSE for two subsamples where predicted utility values were < 0.50 (poor health) or > 0.90 (healthy). External validation was conducted using data from the Child and Adolescent Mental Health Services study.Results: SDQ and CHU9D were moderately correlated (ρ = − 0.52, p < 0.001). EFA demonstrated that all CHU9D domains were associated with four SDQ subscales. The best-performing model was the Generalized Linear Model with SDQ items and gender as predictors (full sample MAE: 0.1149; MSE: 0.0227). The new algorithm performed well in the external validation.Conclusions: The proposed mapping algorithm can produce robust estimates of CHU9D utilities from SDQ data for economic evaluations. Further research is warranted to assess the applicability of the algorithm among children with severe health problems.
KW - CHU9D
KW - Mapping
KW - SDQ
KW - Utility
UR - http://www.scopus.com/inward/record.url?scp=85066793204&partnerID=8YFLogxK
U2 - 10.1007/s11136-019-02220-x
DO - 10.1007/s11136-019-02220-x
M3 - Article
VL - 28
SP - 2429
EP - 2441
JO - Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
T2 - Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
JF - Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
SN - 0962-9343
IS - 9
ER -