Abstract
Background
Utility scores for use in cost-utility analysis may be imputed from the SF-36 health instrument using various techniques, typically regression analysis. This paper explored imputation using partial credit Rasch analysis.
Method
Data from the Assessment of Quality of Life (AQoL) instrument validation study were re-analysed (n = 996 inpatients, outpatients and a community sample). For each AQoL item, factor analysis identified those SF-36 items forming a unidimensional scale. Rasch analysis located scale logit scores for these SF-36 items. The logit scores were used to assign AQoL item scores. The standard AQoL scoring algorithm was then applied to obtain the utility scores.
Results
Many SF-36 items were limited predictors of AQoL items; some items from both instruments obtained disordered thresholds. All imputed scores were consistent with the AQoL model and fell within AQoL score boundaries. The explained variance between imputed and true AQoL scores was 61%.
Discussion
Rasch-imputed mapping, unlike many regression-based algorithms, produced results consistent with the axioms of utility measurement, while the proportion of explained variance was similar to regression-based modelling. Item properties on both instruments implied that some items should be revised using Rasch analysis. The methods and results may be used by researchers needing to impute utility scores from SF-36 health scores.
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Abbreviations
- AQoL:
-
Assessment of Quality of Life multi-attribute utility instrument
- CUA:
-
Cost-utility analysis
- DIF:
-
Differential item functioning
- EFA:
-
Exploratory factor analysis
- ICC:
-
Intra-class correlation
- MAU:
-
Multi-attribute utility
- QALY:
-
Quality-adjusted life year
- SF-36:
-
Short Form-36 health survey
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Acknowledgements
The research reported in this paper was supported by an Australian National Health and Medical Research Council (NHMRC) Project Grant, the Department of Psychiatry in the Faculty of Medicine, Dentistry and Health Sciences at The University of Melbourne and the Centre for Health Economics at Monash University. Much of this work was completed while Professor Leonie Segal was working at the Centre for Health Economics at Monash University prior to taking up her current position as the Professor of Health Economics, Division of Health Sciences, University of South Australia. The views expressed herein are the sole responsibility of the authors. The authors have no competing interests to declare.
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Hawthorne, G., Densley, K., Pallant, J.F. et al. Deriving utility scores from the SF-36 health instrument using Rasch analysis. Qual Life Res 17, 1183–1193 (2008). https://doi.org/10.1007/s11136-008-9395-5
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DOI: https://doi.org/10.1007/s11136-008-9395-5