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Deriving utility scores from the SF-36 health instrument using Rasch analysis

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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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Authors and Affiliations

  1. Department of Psychiatry, Level 1 North, Main Building, Royal Melbourne Hospital, Grattan Street, Parkville, Victoria, 3050, Australia

    Graeme Hawthorne

  2. Department of Psychiatry, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Australia

    Graeme Hawthorne & Konstancja Densley

  3. School of Rural Health, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Shepparton, Australia

    Julie F. Pallant

  4. Centre for Health Economics, Faculty of Business and Economics, Monash University, Melbourne, Australia

    Duncan Mortimer

  5. Division of Health Sciences, University of South Australia, Adelaide, Australia

    Leonie Segal

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  1. Graeme Hawthorne
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  2. Konstancja Densley
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  3. Julie F. Pallant
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  4. Duncan Mortimer
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  5. Leonie Segal
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Correspondence to Graeme Hawthorne.

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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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