IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v44y2024i3p239-251.html
   My bibliography  Save this article

A Tutorial on Net Benefit Regression for Real-World Cost-Effectiveness Analysis Using Censored Data from Randomized or Observational Studies

Author

Listed:
  • Shuai Chen

    (Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
    Center for Healthcare Policy and Research, University of California, Davis, Sacramento, CA, USA)

  • Heejung Bang

    (Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
    Center for Healthcare Policy and Research, University of California, Davis, Sacramento, CA, USA)

  • Jeffrey S. Hoch

    (Division of Health Policy and Management, Department of Public Health Sciences, University of California, Davis, Sacramento, CA, USA
    Center for Healthcare Policy and Research, University of California, Davis, Sacramento, CA, USA)

Abstract

Given the increasing popularity of person-level cost-effectiveness analysis using “real-world†data, there is a clear need to understand and use methods for observational data. When the cost-effectiveness data are subject to censoring, ignoring censoring is especially error prone for heavily censored data. We summarize best practice and provide a hands-on example of applying the net benefit regression framework for cost-effectiveness analysis, which works for both observational and randomized studies with possibly censored data. Many existing methods are special cases within this framework. We provide step-by-step guidance, user-friendly R programs, and examples to illustrate 1) fitting net benefit regressions for possibly censored cost-effectiveness data; 2) implementing doubly robust methods combining net benefit regressions and propensity scores, which may increase the chances to obtain consistent estimates in observational studies; 3) constructing cost-effectiveness acceptability curves; and 4) interpreting the results. The methods in this tutorial are easy to use and lead to more reliable and robust results using typical administrative data, thus providing an attractive option for real-world cost-effectiveness analysis using possibly censored observational data sets. Highlights We illustrate the steps involved in carrying out cost-effectiveness analysis using net benefit regressions with possibly censored demo data by providing step-by-step guidance and code applied to a data set. We demonstrate the importance of these new methods by illustrating how naïve methods for handling censoring can lead to biased cost-effectiveness results.

Suggested Citation

  • Shuai Chen & Heejung Bang & Jeffrey S. Hoch, 2024. "A Tutorial on Net Benefit Regression for Real-World Cost-Effectiveness Analysis Using Censored Data from Randomized or Observational Studies," Medical Decision Making, , vol. 44(3), pages 239-251, April.
    • Handle: RePEc:sae:medema:v:44:y:2024:i:3:p:239-251
    DOI: 10.1177/0272989X241230071
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X241230071
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X241230071?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. Ben A. Van Hout & Maiwenn J. Al & Gilad S. Gordon & Frans F. H. Rutten, 1994. "Costs, effects and C/E‐ratios alongside a clinical trial," Health Economics, John Wiley & Sons, Ltd., vol. 3(5), pages 309-319, September.
    Full references (including those not matched with items on IDEAS)

    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. Monica Merito & Patrizio Pezzotti, 2006. "Comparing costs and effectiveness of different starting points for highly active antiretroviral therapy in HIV-positive patients," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 7(1), pages 30-36, March.
    2. Rachael L. Fleurence, 2007. "Setting priorities for research: a practical application of 'payback' and expected value of information," Health Economics, John Wiley & Sons, Ltd., vol. 16(12), pages 1345-1357.
    3. Karl Claxton & Elisabeth Fenwick & Mark J. Sculpher, 2012. "Decision-making with Uncertainty: The Value of Information," Chapters, in: Andrew M. Jones (ed.), The Elgar Companion to Health Economics, Second Edition, chapter 51, Edward Elgar Publishing.
    4. Andrija S Grustam & Nasuh Buyukkaramikli & Ron Koymans & Hubertus J M Vrijhoef & Johan L Severens, 2019. "Value of information analysis in telehealth for chronic heart failure management," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-23, June.
    5. John Mullahy, 2017. "Individual Results May Vary: Elementary Analytics of Inequality-Probability Bounds, with Applications to Health-Outcome Treatment Effects," NBER Working Papers 23603, National Bureau of Economic Research, Inc.
    6. Andrew Briggs, 2012. "Statistical Methods for Cost-effectiveness Analysis Alongside Clinical Trials," Chapters, in: Andrew M. Jones (ed.), The Elgar Companion to Health Economics, Second Edition, chapter 50, Edward Elgar Publishing.
    7. Pedram Sendi, 2021. "Dealing with Bad Risk in Cost-Effectiveness Analysis: The Cost-Effectiveness Risk-Aversion Curve," PharmacoEconomics, Springer, vol. 39(2), pages 161-169, February.
    8. Gafni, Amiram & Birch, Stephen, 2006. "Incremental cost-effectiveness ratios (ICERs): The silence of the lambda," Social Science & Medicine, Elsevier, vol. 62(9), pages 2091-2100, May.
    9. Kobelt, G., 2013. "Health Economics: An Introduction to Economic Evaluation," Monographs, Office of Health Economics, number 000004.
    10. Ana P. Johnson-Masotti & Purushottam W. Laud & Raymond G. Hoffmann & Matthew J. Hayat & Steven D. Pinkerton, 2004. "A Bayesian Approach to Net Health Benefits: An Illustration and Application to Modeling HIV Prevention," Medical Decision Making, , vol. 24(6), pages 634-653, November.
    11. Els Meeuwsen & René Melis & Geert van der Aa & Gertie Golüke-Willemse & Benoit de Leest & Frank van Raak & Carla Schölzel-Dorenbos & Desiree Verheijen & Frans Verhey & Marieke Visser & Claire Wolfs & , 2013. "Cost-Effectiveness of One Year Dementia Follow-Up Care by Memory Clinics or General Practitioners: Economic Evaluation of a Randomised Controlled Trial," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-7, November.
    12. Barbara Barrett & Waquas Waheed & Simone Farrelly & Max Birchwood & Graham Dunn & Clare Flach & Claire Henderson & Morven Leese & Helen Lester & Max Marshall & Diana Rose & Kim Sutherby & George Szmuk, 2013. "Randomised Controlled Trial of Joint Crisis Plans to Reduce Compulsory Treatment for People with Psychosis: Economic Outcomes," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-11, November.
    13. Bas Groot Koerkamp & Milton C. Weinstein & Theo Stijnen & M.H. Heijenbrok-Kal & M.G. Myriam Hunink, 2010. "Uncertainty and Patient Heterogeneity in Medical Decision Models," Medical Decision Making, , vol. 30(2), pages 194-205, March.
    14. David Meads & Andrea Marshall & Claire Hulme & Janet Dunn & Hugo Ford, 2016. "The Cost Effectiveness of Docetaxel and Active Symptom Control versus Active Symptom Control Alone for Refractory Oesophagogastric Adenocarcinoma: Economic Analysis of the COUGAR-02 Trial," PharmacoEconomics, Springer, vol. 34(1), pages 33-42, January.
    15. Andrew Briggs, 1995. "Handling Uncertainty in the Results of Economic Evaluation," Briefing 000410, Office of Health Economics.
    16. Michał Jakubczyk & Bogumił Kamiński, 2017. "Fuzzy approach to decision analysis with multiple criteria and uncertainty in health technology assessment," Annals of Operations Research, Springer, vol. 251(1), pages 301-324, April.
    17. Edmond S.-W. Ng & Richard Grieve & James R. Carpenter, 2013. "Two-stage nonparametric bootstrap sampling with shrinkage correction for clustered data," Stata Journal, StataCorp LP, vol. 13(1), pages 141-164, March.
    18. Pedram Sendi & Huldrych F Günthard & Mathew Simcock & Bruno Ledergerber & Jörg Schüpbach & Manuel Battegay & for the Swiss HIV Cohort Study, 2007. "Cost-Effectiveness of Genotypic Antiretroviral Resistance Testing in HIV-Infected Patients with Treatment Failure," PLOS ONE, Public Library of Science, vol. 2(1), pages 1-8, January.
    19. Andrew Briggs & Paul Fenn, 1998. "Confidence intervals or surfaces? Uncertainty on the cost‐effectiveness plane," Health Economics, John Wiley & Sons, Ltd., vol. 7(8), pages 723-740, December.
    20. Egil Kjerstad & Hanne Kristin Tuntland, 2016. "Reablement in community-dwelling older adults: a cost-effectiveness analysis alongside a randomized controlled trial," Health Economics Review, Springer, vol. 6(1), pages 1-10, December.

    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:44:y:2024:i:3:p:239-251. 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.