IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v31y2011i6p790-799.html

Calculating Utility Decrements Associated With an Adverse Event

Author

Listed:
  • Eleanor M. Pullenayegum
  • Jean-Eric Tarride
  • Feng Xie
  • Daria O’Reilly

Abstract

Background: When calculating the decreases in health utility associated with adverse events, often a number ofrespondents achieve the upper utility bound of 1. “Marginal†Tobit or CLAD coefficients have been used to account for this. These are calculated by using a Tobit or a CLAD model to estimate the decrease in a latent unbounded variable associated with the event or condition, then to multiply by the proportion of respondents falling below 1 in order to transform back to the utility scale. Objective & Methods: Starting with the Tobit model, we show mathematically that this procedure is not valid, when calculating decreases in utility associated with binary events. We then generalize the result to the CLAD model. A selection of published studies is used to illustrate the bias in the marginal Tobit decrements. Results: The degree of bias is more severe the greater the decrease in utility associated with the event, and the larger the proportion of individuals at the upper ceiling.In the examples studied, the degree of bias was often greater than 10%. We provide the correct formula for calculating the utility decrement. Conclusions: The marginal Tobit and CLAD coefficients should not be used as estimates of a utility decrement corresponding to an adverse event or health condition unless the coefficients are small in absolute value, or if the proportion of individuals at the upper utility bound is small. In other settings, the corrected formula or alternative regression methods (e.g. linear models of mean utility) should be considered.

Suggested Citation

  • Eleanor M. Pullenayegum & Jean-Eric Tarride & Feng Xie & Daria O’Reilly, 2011. "Calculating Utility Decrements Associated With an Adverse Event," Medical Decision Making, , vol. 31(6), pages 790-799, November.
  • Handle: RePEc:sae:medema:v:31:y:2011:i:6:p:790-799
    DOI: 10.1177/0272989X10393284
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X10393284
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X10393284?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
    ---><---

    References listed on IDEAS

    as
    1. Greene, William, 1999. "Marginal effects in the censored regression model," Economics Letters, Elsevier, vol. 64(1), pages 43-49, July.
    2. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
    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. John Yfantopoulos & Athanasios Chantzaras, 2020. "Health-related quality of life and health utilities in insulin-treated type 2 diabetes: the impact of related comorbidities/complications," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(5), pages 729-743, July.
    2. Charles Christian Adarkwah & Amirhossein Sadoghi & Afschin Gandjour, 2016. "Should Cost‐Effectiveness Analysis Include the Cost of Consumption Activities? AN Empirical Investigation," Health Economics, John Wiley & Sons, Ltd., vol. 25(2), pages 249-256, February.

    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. Sarah Brown & Karl Taylor, 2008. "Household debt and financial assets: evidence from Germany, Great Britain and the USA," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 615-643, June.
    2. Myoung-jae Lee, 2017. "Extensive and intensive margin effects in sample selection models: racial effects on wages," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 817-839, June.
    3. Joel L. Horowitz, 1998. "Bootstrap Methods for Median Regression Models," Econometrica, Econometric Society, vol. 66(6), pages 1327-1352, November.
    4. Héctor Manuel Zárate S., 2005. "Cambios en la estructura salarial: una historia desde la regresión cuanfílica," Monetaria, CEMLA, vol. 0(4), pages 339-364, octubre-d.
    5. Xianghua Luo & Chiung-Yu Huang & Lan Wang, 2013. "Quantile Regression for Recurrent Gap Time Data," Biometrics, The International Biometric Society, vol. 69(2), pages 375-385, June.
    6. Xiaohong Chen & Oliver Linton & Ingrid Van Keilegom, 2003. "Estimation of Semiparametric Models when the Criterion Function Is Not Smooth," Econometrica, Econometric Society, vol. 71(5), pages 1591-1608, September.
    7. Andrés Langebaek R. & Diego Vásquez E., 2007. "Determinantes de la actividad innovadora en la industria manufacturera colombiana," Borradores de Economia 433, Banco de la Republica de Colombia.
    8. Peracchi, Franco, 2002. "On estimating conditional quantiles and distribution functions," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 433-447, February.
    9. Erik Figueiredo & Luiz Renato Lima & Gianluca Orefice, 2016. "Migration and Regional Trade Agreements: A (New) Gravity Estimation," Review of International Economics, Wiley Blackwell, vol. 24(1), pages 99-125, February.
    10. Cizek, P., 2009. "Generalized Methods of Trimmed Moments," Discussion Paper 2009-25, Tilburg University, Center for Economic Research.
    11. Mark J. Garmaise & Tobias J. Moskowitz, 2002. "Confronting Information Asymmetries: Evidence from Real Estate Markets," NBER Working Papers 8877, National Bureau of Economic Research, Inc.
    12. Akbar Marvasti, 2007. "Foreign-Born Teaching Assistants and Student Achievement: An Ordered Probit Analysis," The American Economist, Sage Publications, vol. 51(2), pages 61-71, October.
    13. Adelino, Manuel & Dinc, I. Serdar, 2014. "Corporate distress and lobbying: Evidence from the Stimulus Act," Journal of Financial Economics, Elsevier, vol. 114(2), pages 256-272.
    14. Gerrans, Paul & Yap, Ghialy, 2014. "Retirement savings investment choices: Sophisticated or naive?," Pacific-Basin Finance Journal, Elsevier, vol. 30(C), pages 233-250.
    15. Takalo, Tuomas & Tanayama, Tanja & Toivanen, Otto, 2008. "Evaluating innovation policy: a structural treatment effect model of R&D subsidies," Bank of Finland Research Discussion Papers 7/2008, Bank of Finland.
    16. Brunner, Eric & Sonstelie, Jon, 2003. "School finance reform and voluntary fiscal federalism," Journal of Public Economics, Elsevier, vol. 87(9-10), pages 2157-2185, September.
    17. Jalan, Jyotsna & Ravallion, Martin, 2001. "Behavioral responses to risk in rural China," Journal of Development Economics, Elsevier, vol. 66(1), pages 23-49, October.
    18. Myoung‐jae Lee & Young‐sook Kim, 2012. "Zero‐Inflated Endogenous Count In Censored Model: Effects Of Informal Family Care On Formal Health Care," Health Economics, John Wiley & Sons, Ltd., vol. 21(9), pages 1119-1133, September.
    19. Eliana Christou & Michael G. Akritas, 2019. "Single index quantile regression for censored data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(4), pages 655-678, December.
    20. Kalyan Chakraborty & John Poggio, 2008. "Efficiency and Equity in School Funding: A Case Study for Kansas," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 14(2), pages 228-241, May.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:sae:medema:v:31:y:2011:i:6:p:790-799. 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: SAGE Publications (email available below). General contact details of provider: .

    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.