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    Predicting health-related quality of life (EQ-5D-5 L) and capability wellbeing (ICECAP-A) in the context of opiate dependence using routine clinical outcome measures: CORE-OM, LDQ and TOP

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    Author
    Peak, J; Goranitis, I; Day, E; Copello, A; Freemantle, N; Frew, E
    Date
    2018-05-30
    Source Title
    Health and Quality of Life Outcomes
    Publisher
    BIOMED CENTRAL LTD
    University of Melbourne Author/s
    Goranitis, Ilias
    Affiliation
    Melbourne School of Population and Global Health
    Metadata
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    Document Type
    Journal Article
    Citations
    Peak, J., Goranitis, I., Day, E., Copello, A., Freemantle, N. & Frew, E. (2018). Predicting health-related quality of life (EQ-5D-5 L) and capability wellbeing (ICECAP-A) in the context of opiate dependence using routine clinical outcome measures: CORE-OM, LDQ and TOP. HEALTH AND QUALITY OF LIFE OUTCOMES, 16 (1), https://doi.org/10.1186/s12955-018-0926-7.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/255176
    DOI
    10.1186/s12955-018-0926-7
    Abstract
    BACKGROUND: Economic evaluation normally requires information to be collected on outcome improvement using utility values. This is often not collected during the treatment of substance use disorders making cost-effectiveness evaluations of therapy difficult. One potential solution is the use of mapping to generate utility values from clinical measures. This study develops and evaluates mapping algorithms that could be used to predict the EuroQol-5D (EQ-5D-5 L) and the ICEpop CAPability measure for Adults (ICECAP-A) from the three commonly used clinical measures; the CORE-OM, the LDQ and the TOP measures. METHODS: Models were estimated using pilot trial data of heroin users in opiate substitution treatment. In the trial the EQ-5D-5 L, ICECAP-A, CORE-OM, LDQ and TOP were administered at baseline, three and twelve month time intervals. Mapping was conducted using estimation and validation datasets. The normal estimation dataset, which comprised of baseline sample data, used ordinary least squares (OLS) and tobit regression methods. Data from the baseline and three month time periods were combined to create a pooled estimation dataset. Cluster and mixed regression methods were used to map from this dataset. Predictive accuracy of the models was assessed using the root mean square error (RMSE) and the mean absolute error (MAE). Algorithms were validated using sample data from the follow-up time periods. RESULTS: Mapping algorithms can be used to predict the ICECAP-A and the EQ-5D-5 L in the context of opiate dependence. Although both measures can be predicted, the ICECAP-A was better predicted by the clinical measures. There were no advantages of pooling the data. There were 6 chosen mapping algorithms, which had MAE scores ranging from 0.100 to 0.138 and RMSE scores ranging from 0.134 to 0.178. CONCLUSION: It is possible to predict the scores of the ICECAP-A and the EQ-5D-5 L with the use of mapping. In the context of opiate dependence, these algorithms provide the possibility of generating utility values from clinical measures and thus enabling economic evaluation of alternative therapy options. TRIAL REGISTRATION: ISRCTN22608399 . Date of registration: 27/04/2012. Date of first randomisation: 14/08/2012.

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