IDEAS home Printed from https://ideas.repec.org/a/spr/patien/v14y2021i6d10.1007_s40271-021-00518-y.html
   My bibliography  Save this article

The Impact of the Risk Functional Form Assumptions on Maximum Acceptable Risk Measures

Author

Listed:
  • Juan Marcos Gonzalez

    (Duke University)

  • Marco Boeri

    (RTI Health Solutions)

Abstract

Background Discrete-choice experiments (DCEs) are increasingly conducted to quantify risk tolerance by computing maximum acceptable risk (MAR) for improvements in efficacy or other benefits gained from new medical treatments. To compute MARs from DCE data, respondents are asked to make choices under uncertainty between treatments. Specific treatment-related harms are included in the choice questions as probabilistic adverse events (AEs), and choice variation with the probability of these outcomes is assumed to indicate their effect on the expected utility of treatments. With a limited number of comparisons between profiles, calculation of MARs requires understanding how outcome probabilities that are not explicitly considered in the DCE can change the value of medical technologies. This study aims to examine how various assumptions on the expected disutility of these excluded probabilities can result in different MAR measures. Methods We summarize commonly used empirical specifications for the expected disutility of AEs and derive the resulting MAR functions. We then discuss an empirical application on treatments to delay bone metastases in oncology patients with solid tumors. Results A total of 187 respondents completed the DCE. Results show the impact of making various assumptions about the expected disutility of AEs, and the resulting MAR values for specific health benefits. As expected, different assumptions resulted in variations in MAR values for specific health benefits. Even with small differences in MAR measures, our results suggest that the assumptions evaluated here can lead to different conclusions about the acceptability of a medical technology. Conclusion Results show possible systematic variations in MARs caused by the assumed form of the effect of changes in the probability of AEs. Furthermore, we find that different assumptions can lead to different conclusions about the acceptability of a medical technology, even when MAR distributions overlap. This result suggests that researchers should evaluate the assumptions they are making for these effects and use sensitivity analysis to evaluate the robustness of risk-tolerance measures from stated-preference data.

Suggested Citation

  • Juan Marcos Gonzalez & Marco Boeri, 2021. "The Impact of the Risk Functional Form Assumptions on Maximum Acceptable Risk Measures," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 14(6), pages 827-836, November.
    • Handle: RePEc:spr:patien:v:14:y:2021:i:6:d:10.1007_s40271-021-00518-y
    DOI: 10.1007/s40271-021-00518-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40271-021-00518-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40271-021-00518-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yi Qian & Jorge Arellano & A. Brett Hauber & Ateesha F. Mohamed & Juan Marcos Gonzalez & Guy Hechmati & Francesca Gatta & Stacey Harrelson & Cynthia Campbell-Baird, 2016. "Patient, Caregiver, and Nurse Preferences for Treatments for Bone Metastases from Solid Tumors," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 9(4), pages 323-333, August.
    2. A. Brett Hauber & Angelyn Fairchild & F. Reed Johnson, 2013. "Quantifying Benefit–Risk Preferences for Medical Interventions: An Overview of a Growing Empirical Literature," Applied Health Economics and Health Policy, Springer, vol. 11(4), pages 319-329, August.
    3. Arne Risa Hole, 2007. "A comparison of approaches to estimating confidence intervals for willingness to pay measures," Health Economics, John Wiley & Sons, Ltd., vol. 16(8), pages 827-840, August.
    4. George Van Houtven & F. Reed Johnson & Vikram Kilambi & A. Brett Hauber, 2011. "Eliciting Benefit–Risk Preferences and Probability-Weighted Utility Using Choice-Format Conjoint Analysis," Medical Decision Making, , vol. 31(3), pages 469-480, May.
    5. Han Bleichrodt & Jose Maria Abellan-Perpiñan & Jose Luis Pinto-Prades & Ildefonso Mendez-Martinez, 2007. "Resolving Inconsistencies in Utility Measurement Under Risk: Tests of Generalizations of Expected Utility," Management Science, INFORMS, vol. 53(3), pages 469-482, March.
    6. Drazen Prelec, 1998. "The Probability Weighting Function," Econometrica, Econometric Society, vol. 66(3), pages 497-528, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gonzalez Sepulveda, Juan Marcos & Van Houtven, George & Reed, Shelby D. & Webster, Scott & Johnson, F. Reed, 2024. "The impact of violations of expected utility theory on choices in the face of multiple risks," Journal of choice modelling, Elsevier, vol. 53(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Angelyn Otteson Fairchild & Shelby D. Reed & Juan Marcos Gonzalez, 2023. "Method for Calculating the Simultaneous Maximum Acceptable Risk Threshold (SMART) from Discrete-Choice Experiment Benefit-Risk Studies," Medical Decision Making, , vol. 43(2), pages 227-238, February.
    2. Gonzalez Sepulveda, Juan Marcos & Van Houtven, George & Reed, Shelby D. & Webster, Scott & Johnson, F. Reed, 2024. "The impact of violations of expected utility theory on choices in the face of multiple risks," Journal of choice modelling, Elsevier, vol. 53(C).
    3. Abdellaoui, Mohammed & Kemel, Emmanuel & Panin, Amma & Vieider, Ferdinand M., 2019. "Measuring time and risk preferences in an integrated framework," Games and Economic Behavior, Elsevier, vol. 115(C), pages 459-469.
    4. James Andreoni & Charles Sprenger, 2011. "Uncertainty Equivalents: Testing the Limits of the Independence Axiom," NBER Working Papers 17342, National Bureau of Economic Research, Inc.
    5. Arthur E. Attema & Han Bleichrodt & Olivier L'Haridon, 2018. "Ambiguity preferences for health," Health Economics, John Wiley & Sons, Ltd., vol. 27(11), pages 1699-1716, November.
    6. Ranoua Bouchouicha & Ferdinand M. Vieider, 2017. "Accommodating stake effects under prospect theory," Journal of Risk and Uncertainty, Springer, vol. 55(1), pages 1-28, August.
    7. Jose-Luis Pinto-Prades & Jose-Maria Abellan-Perpiñan, 2012. "When normative and descriptive diverge: how to bridge the difference," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 38(4), pages 569-584, April.
    8. Mohammed Abdellaoui & Ahmed Driouchi & Olivier L’Haridon, 2011. "Risk aversion elicitation: reconciling tractability and bias minimization," Theory and Decision, Springer, vol. 71(1), pages 63-80, July.
    9. George Van Houtven & F. Reed Johnson & Vikram Kilambi & A. Brett Hauber, 2011. "Eliciting Benefit–Risk Preferences and Probability-Weighted Utility Using Choice-Format Conjoint Analysis," Medical Decision Making, , vol. 31(3), pages 469-480, May.
    10. Shi, Yun & Cui, Xiangyu & Zhou, Xunyu, 2020. "Beta and Coskewness Pricing: Perspective from Probability Weighting," SocArXiv 5rqhv, Center for Open Science.
    11. Goeree, Jacob K. & Holt, Charles A. & Palfrey, Thomas R., 2002. "Quantal Response Equilibrium and Overbidding in Private-Value Auctions," Journal of Economic Theory, Elsevier, vol. 104(1), pages 247-272, May.
    12. Alex Stomper & Marie-Louise Vierø, 2015. "Iterated Expectations Under Rank-dependent Expected Utility And Model Consistency," Working Paper 1228, Economics Department, Queen's University.
    13. Filiz-Ozbay, Emel & Guryan, Jonathan & Hyndman, Kyle & Kearney, Melissa & Ozbay, Erkut Y., 2015. "Do lottery payments induce savings behavior? Evidence from the lab," Journal of Public Economics, Elsevier, vol. 126(C), pages 1-24.
    14. Melanie Lefevre, 2011. "Willingness-to-pay for Local Milk-based Dairy Product in Senegal," CREPP Working Papers 1108, Centre de Recherche en Economie Publique et de la Population (CREPP) (Research Center on Public and Population Economics) HEC-Management School, University of Liège.
    15. Jianhua Wang & Jiaye Ge & Yuting Ma, 2018. "Urban Chinese Consumers’ Willingness to Pay for Pork with Certified Labels: A Discrete Choice Experiment," Sustainability, MDPI, vol. 10(3), pages 1-14, February.
    16. Armantier, Olivier & Treich, Nicolas, 2013. "Eliciting beliefs: Proper scoring rules, incentives, stakes and hedging," European Economic Review, Elsevier, vol. 62(C), pages 17-40.
    17. Scaccia, Luisa & Marcucci, Edoardo & Gatta, Valerio, 2023. "Prediction and confidence intervals of willingness-to-pay for mixed logit models," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 54-78.
    18. Alarie, Yves & Dionne, Georges, 2005. "Testing explanations of preference reversal: A model," Working Papers 05-2, HEC Montreal, Canada Research Chair in Risk Management.
    19. Mohammed Abdellaoui & Olivier L’Haridon & Horst Zank, 2010. "Separating curvature and elevation: A parametric probability weighting function," Journal of Risk and Uncertainty, Springer, vol. 41(1), pages 39-65, August.
    20. Foster, Gigi & Frijters, Paul & Schaffner, Markus & Torgler, Benno, 2018. "Expectation formation in an evolving game of uncertainty: New experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 154(C), pages 379-405.

    More about this item

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:patien:v:14:y:2021:i:6:d:10.1007_s40271-021-00518-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.