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A review and meta-analysis of health state utility values in breast cancer

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  • Peasgood, T
  • Ward, S
  • Brazier, J

Abstract

Background and purpose Health-related quality of life is an important issue in the treatment of breast cancer, and health-state utilities are essential for cost-utility analysis. This paper identifies and summarises published utilities for common health-related quality of life outcomes for breast cancer, considers the impact of variation in study designs used, and pools utilities for some breast cancer health states. Data sources and study selection Thirteen databases were searched using key words relating to breast cancer and utility measurement. Articles were included if specified empirical methods for deriving utility values were used and details of the method, including number of respondents, were given. Articles were excluded if values were based on expert opinion or were not unique. Data extraction and synthesis The authors identified 49 articles which met their inclusion criteria, providing 476 unique utilities for breast cancer health states. Where possible, mean utility estimates were pooled using ordinary least squares, with utilities clustered within study group and weighted by both number of respondents and inverse of the variance of each utility. Regressions included controls for disease state, utility assessment method and other features of study design. Results Utility values found in the review are summarised for six categories: 1) screening related states, 2) preventative states, 3) adverse events in breast cancer and its treatment, 4) non-specific breast cancer, 5) metastatic breast cancer states and 6) early breast cancer states. Pooled utility values for the latter two categories are estimated, showing base state utility values of between 0.668 and 0.782 for early breast cancer and 0.721 and 0.806 metastatic breast cancer depending upon which model is used. Utilities were found to vary significantly by valuation method, and who conducted the valuation. Conclusions A large number of utility values for breast cancer is available in the literature; the states which these refer to are often complex, making pooling of values problematic. The impacts upon quality of life and length of life are both important to the assessment of treatments for breast cancer. These outcomes can be combined using the health-related quality of life measure of a QALY (quality adjusted life year). QALYs may be thought of as a "utility" score, since they represent people’s preferences towards a particular health state, where 0 represents dead and 1 represents full health. Being able to locate any health state on a 0 to 1 scale allows an estimation of the number of QALYs a treatment brings, and, subsequently, a comparison of the cost per QALY benefit across different treatments. The cost per QALY of competing treatments can be a useful input into medical decision making and priority setting. Cost per QALY for breast cancer treatments may be derived from primary research, or from modelling interventions at different disease stages. Where modelling is conducted, modellers require a "utility" value for each possible health state: e.g. newly diagnosed breast cancer, currently undergoing chemotherapy, and experiencing some toxicity from treatment. This allows them to map the profile of hypothetical patients as they pass through different scenarios and understand the QALYs gained from alternative treatments. There are numerous studies which have investigated the utility values associated with breast cancer; unfortunately, they show considerable variation in results. For example, values for metastatic breast cancer (MBC) range from -0.52 to 0.882. What explains this variation? First, there are a number of different health states which an individual with MBC may experience relating to different treatment regimes, different responses to treatment and different possible side-effects of treatment. Secondly, there are different methods for generating utility scores, which can generate different values for the exact same health state. This study aims to systematically review health state utility values (HSUVs) for breast cancer (early and metastatic) in order to identify all breast cancer HSUVs in the current literature. It then seeks to provide a pooled estimate of HSUVs for each identifiable health state within breast cancer. It also seeks to understand the impact of different methodological techniques on the estimates of utility scores for breast cancer. This will generate a list of HSUVs that can be used in future economic evaluations, and offer greater understanding of how representative individual utility estimates are for breast cancer states.

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  • Peasgood, T & Ward, S & Brazier, J, 2010. "A review and meta-analysis of health state utility values in breast cancer," MPRA Paper 29950, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:29950
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    3. Khadka, Jyoti & Kwon, Joseph & Petrou, Stavros & Lancsar, Emily & Ratcliffe, Julie, 2019. "Mind the (inter-rater) gap. An investigation of self-reported versus proxy-reported assessments in the derivation of childhood utility values for economic evaluation: A systematic review," Social Science & Medicine, Elsevier, vol. 240(C).
    4. Gregory Katz & Olivier Romano & Cyril Foa & Anne-Lise Vataire & Jean-Victor Chantelard & Robert Hervé & Hugues Barletta & Axel Durieux & Jean-Pierre Martin & Rémy Salmon, 2015. "Economic Impact of Gene Expression Profiling in Patients with Early-Stage Breast Cancer in France," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-15, June.
    5. Quang A. Le, 2016. "Structural Uncertainty of Markov Models for Advanced Breast Cancer," Medical Decision Making, , vol. 36(5), pages 629-640, July.
    6. Bromley, Hannah L. & Petrie, Dennis & Mann, G.Bruce & Nickson, Carolyn & Rea, Daniel & Roberts, Tracy E., 2019. "Valuing the health states associated with breast cancer screening programmes: A systematic review of economic measures," Social Science & Medicine, Elsevier, vol. 228(C), pages 142-154.
    7. Micah Rose & Stephen Rice & Dawn Craig, 2018. "Does Methodological Guidance Produce Consistency? A Review of Methodological Consistency in Breast Cancer Utility Value Measurement in NICE Single Technology Appraisals," PharmacoEconomics - Open, Springer, vol. 2(2), pages 97-107, June.
    8. Nathaniel Hendrix & David D. Kim & Krishna S. Patel & Beth Devine, 2021. "Differences in the Selection of Health State Utility Values by Sponsorship in Published Cost-Effectiveness Analyses," Medical Decision Making, , vol. 41(3), pages 366-372, April.
    9. Dirk Müller & Marion Danner & Kerstin Rhiem & Björn Stollenwerk & Christoph Engel & Linda Rasche & Lisa Borsi & Rita Schmutzler & Stephanie Stock, 2018. "Cost-effectiveness of different strategies to prevent breast and ovarian cancer in German women with a BRCA 1 or 2 mutation," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(3), pages 341-353, April.
    10. Caroline S Clarke & Rachael M Hunter & Ian Shemilt & Victoria Serra-Sastre, 2017. "Multi-arm Cost-Effectiveness Analysis (CEA) comparing different durations of adjuvant trastuzumab in early breast cancer, from the English NHS payer perspective," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-19, March.
    11. Dirk Müller & Marion Danner & Rita Schmutzler & Christoph Engel & Kirsten Wassermann & Björn Stollenwerk & Stephanie Stock & Kerstin Rhiem, 2019. "Economic modeling of risk-adapted screen-and-treat strategies in women at high risk for breast or ovarian cancer," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(5), pages 739-750, July.
    12. Claudine Bommer & Judith Lupatsch & Nicole Bürki & Matthias Schwenkglenks, 2022. "Cost–utility analysis of risk-reducing strategies to prevent breast and ovarian cancer in BRCA-mutation carriers in Switzerland," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(5), pages 807-821, July.
    13. Jiryoun Gong & Juhee Han & Donghwan Lee & Seungjin Bae, 2020. "A Meta-Regression Analysis of Utility Weights for Breast Cancer: The Power of Patients’ Experience," IJERPH, MDPI, vol. 17(24), pages 1-16, December.
    14. Roberta Ara & John Brazier & Ismail Azzabi Zouraq, 2017. "The Use of Health State Utility Values in Decision Models," PharmacoEconomics, Springer, vol. 35(1), pages 77-88, December.
    15. Feng Xie & Michael Zoratti & Kelvin Chan & Don Husereau & Murray Krahn & Oren Levine & Tammy Clifford & Holger Schunemann & Gordon Guyatt, 2019. "Toward a Centralized, Systematic Approach to the Identification, Appraisal, and Use of Health State Utility Values for Reimbursement Decision Making: Introducing the Health Utility Book (HUB)," Medical Decision Making, , vol. 39(4), pages 371-379, May.
    16. Sandjar Djalalov & Linda Rabeneck & George Tomlinson & Karen E. Bremner & Robert Hilsden & Jeffrey S. Hoch, 2014. "A Review and Meta-analysis of Colorectal Cancer Utilities," Medical Decision Making, , vol. 34(6), pages 809-818, August.
    17. Patricia Blank & Martin Filipits & Peter Dubsky & Florian Gutzwiller & Michael Lux & Jan Brase & Karsten Weber & Margaretha Rudas & Richard Greil & Sibylle Loibl & Thomas Szucs & Ralf Kronenwett & Mat, 2015. "Cost-Effectiveness Analysis of Prognostic Gene Expression Signature-Based Stratification of Early Breast Cancer Patients," PharmacoEconomics, Springer, vol. 33(2), pages 179-190, February.

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    More about this item

    Keywords

    breast cancer; utility assessment; health-state utility; systematic review; quality of life; meta-analysis; preferences;
    All these keywords.

    JEL classification:

    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • I19 - Health, Education, and Welfare - - Health - - - Other

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