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Estimating utilities from individual health preference data: a nonparametric Bayesian method

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  • Samer A. Kharroubi
  • Anthony O'Hagan
  • John E. Brazier

Abstract

Summary. A fundamental benefit that is conferred by medical treatments is to increase the health‐related quality of life (HRQOL) that is experienced by patients. Various descriptive systems exist to define a patient's health state, and we address the problem of assigning an HRQOL value to any given state in such a descriptive system. Data derive from experiments in which individuals are asked to assign their personal values to various health states. We construct a Bayesian model that takes account of various important aspects of such data. Specifically, we allow for the repeated measures feature that each individual values several different states, and the fact that individuals vary markedly in their valuations, with some people consistently providing higher valuations than others. We model the relationship between HRQOL and health state nonparametrically. We illustrate our method by using data from an experiment in which 611 individuals each valued up to six states in the descriptive system known as the SF‐6D system. Although the SF‐6D system distinguishes 18000 different health states, only 249 of these were valued in this experiment. We provide posterior inference about the HRQOL values for all 18000 states.

Suggested Citation

  • Samer A. Kharroubi & Anthony O'Hagan & John E. Brazier, 2005. "Estimating utilities from individual health preference data: a nonparametric Bayesian method," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(5), pages 879-895, November.
  • Handle: RePEc:bla:jorssc:v:54:y:2005:i:5:p:879-895
    DOI: 10.1111/j.1467-9876.2005.00511.x
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    Cited by:

    1. Samer A. Kharroubi & Yara Beyh & Marwa Diab El Harake & Dalia Dawoud & Donna Rowen & John Brazier, 2020. "Examining the Feasibility and Acceptability of Valuing the Arabic Version of SF-6D in a Lebanese Population," IJERPH, MDPI, vol. 17(3), pages 1-15, February.
    2. Menglu Che & Feng Xie & Stephanie Thomas & Eleanor Pullenayegum, 2023. "Bayesian Models with Spatial Correlation Improve the Precision of EQ-5D-5L Value Sets," Medical Decision Making, , vol. 43(5), pages 587-594, July.
    3. Round, Jeff, 2012. "Is a QALY still a QALY at the end of life?," Journal of Health Economics, Elsevier, vol. 31(3), pages 521-527.
    4. Musal, R. Muzaffer & Soyer, Refik & McCabe, Christopher & Kharroubi, Samer A., 2012. "Estimating the population utility function: A parametric Bayesian approach," European Journal of Operational Research, Elsevier, vol. 218(2), pages 538-547.
    5. Samer Kharroubi, 2015. "A Comparison of Japan and UK SF-6D Health-State Valuations Using a Non-Parametric Bayesian Method," Applied Health Economics and Health Policy, Springer, vol. 13(4), pages 409-420, August.
    6. Samer A. Kharroubi & Donna Rowen, 2019. "Valuation of preference-based measures: can existing preference data be used to select a smaller sample of health states?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(2), pages 245-255, March.
    7. Dan Kelleher & Samer Kharroubi & Edel Doherty & Gianluca Baio & Ciaran O’Neill, 2022. "Examining the Association between Polish Migrant Status and Health Preferences Using a Novel Application of a Smaller Design EQ-5D-5L Valuation Study," PharmacoEconomics - Open, Springer, vol. 6(3), pages 425-435, May.
    8. Kelvin K. W. Chan & Feng Xie & Andrew R. Willan & Eleanor M. Pullenayegum, 2017. "Underestimation of Variance of Predicted Health Utilities Derived from Multiattribute Utility Instruments," Medical Decision Making, , vol. 37(3), pages 262-272, April.
    9. John Brazier & Roberta Ara & Donna Rowen & Helene Chevrou-Severac, 2017. "A Review of Generic Preference-Based Measures for Use in Cost-Effectiveness Models," PharmacoEconomics, Springer, vol. 35(1), pages 21-31, December.
    10. O'Hagan, A & Brazier, JE & Kharroubi, SA, 2007. "A comparison of United States and United Kingdom EQ-5D health states valuations using a nonparametric Bayesian method," MPRA Paper 29806, University Library of Munich, Germany.
    11. Samer A. Kharroubi, 2021. "Modeling SF-6D Health Utilities: Is Bayesian Approach Appropriate?," IJERPH, MDPI, vol. 18(16), pages 1-14, August.
    12. Samer A. Kharroubi & Yara Beyh, 2021. "Bayesian modeling of health state preferences: could borrowing strength from existing countries’ valuations produce better estimates," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(5), pages 773-788, July.
    13. Alexina J. Mason & Manuel Gomes & James Carpenter & Richard Grieve, 2021. "Flexible Bayesian longitudinal models for cost‐effectiveness analyses with informative missing data," Health Economics, John Wiley & Sons, Ltd., vol. 30(12), pages 3138-3158, December.
    14. Hirofumi Nagayama & Kounosuke Tomori & Kanta Ohno & Kayoko Takahashi & Kakuya Ogahara & Tatsunori Sawada & Sei Uezu & Ryutaro Nagatani & Keita Yamauchi, 2016. "Effectiveness and Cost-Effectiveness of Occupation-Based Occupational Therapy Using the Aid for Decision Making in Occupation Choice (ADOC) for Older Residents: Pilot Cluster Randomized Controlled Tri," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-13, March.
    15. Kharroubi, Samer A. & Brazier, John E. & Roberts, Jennifer & O'Hagan, Anthony, 2007. "Modelling SF-6D health state preference data using a nonparametric Bayesian method," Journal of Health Economics, Elsevier, vol. 26(3), pages 597-612, May.
    16. Eleanor M. Pullenayegum & Kelvin K. W. Chan & Feng Xie, 2016. "Quantifying Parameter Uncertainty in EQ-5D-3L Value Sets and Its Impact on Studies That Use the EQ-5D-3L to Measure Health Utility," Medical Decision Making, , vol. 36(2), pages 223-233, February.
    17. Kharroubi, Samer & Brazier, John E. & O'Hagan, Anthony, 2007. "Modelling covariates for the SF-6D standard gamble health state preference data using a nonparametric Bayesian method," Social Science & Medicine, Elsevier, vol. 64(6), pages 1242-1252, March.

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