IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v30y2021i2d10.1007_s10260-020-00539-1.html
   My bibliography  Save this article

On the predictive performance of a non-optimal action in hypothesis testing

Author

Listed:
  • Fulvio De Santis

    (Sapienza Università di Roma)

  • Stefania Gubbiotti

    (Sapienza Università di Roma)

Abstract

In Bayesian decision theory, the performance of an action is measured by its posterior expected loss. In some cases it may be convenient/necessary to use a non-optimal decision instead of the optimal one. In these cases it is important to quantify the additional loss we incur and evaluate whether to use the non-optimal decision or not. In this article we study the predictive probability distribution of a relative measure of the additional loss and its use to define sample size determination criteria in a general testing set-up.

Suggested Citation

  • Fulvio De Santis & Stefania Gubbiotti, 2021. "On the predictive performance of a non-optimal action in hypothesis testing," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 689-709, June.
    • Handle: RePEc:spr:stmapp:v:30:y:2021:i:2:d:10.1007_s10260-020-00539-1
    DOI: 10.1007/s10260-020-00539-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-020-00539-1
    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/s10260-020-00539-1?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. Fulvio De Santis & Stefania Gubbiotti, 2017. "A decision‐theoretic approach to sample size determination under several priors," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(3), pages 282-295, May.
    2. M'Lan, Cyr Emile & Joseph, Lawrence & Wolfson, David B., 2006. "Bayesian Sample Size Determination for Case-Control Studies," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 760-772, June.
    3. Pierpaolo Brutti & Fulvio Santis & Stefania Gubbiotti, 2014. "Bayesian-frequentist sample size determination: a game of two priors," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 133-151, August.
    4. De Santis, Fulvio, 2006. "Sample Size Determination for Robust Bayesian Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 278-291, March.
    5. S. K. Sahu & T. M. F. Smith, 2006. "A Bayesian method of sample size determination with practical applications," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(2), pages 235-253, March.
    6. Anthony O’Hagan & John W. Stevens, 2001. "Bayesian Assessment of Sample Size for Clinical Trials of Cost-Effectiveness," Medical Decision Making, , vol. 21(3), pages 219-230, May.
    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. Fulvio De Santis & Stefania Gubbiotti, 2021. "Sample Size Requirements for Calibrated Approximate Credible Intervals for Proportions in Clinical Trials," IJERPH, MDPI, vol. 18(2), pages 1-11, January.
    2. Pierpaolo Brutti & Fulvio Santis & Stefania Gubbiotti, 2014. "Bayesian-frequentist sample size determination: a game of two priors," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 133-151, August.
    3. Armando Turchetta & Erica E. M. Moodie & David A. Stephens & Sylvie D. Lambert, 2023. "Bayesian sample size calculations for comparing two strategies in SMART studies," Biometrics, The International Biometric Society, vol. 79(3), pages 2489-2502, September.
    4. Ali Karimnezhad & Ahmad Parsian, 2018. "Most stable sample size determination in clinical trials," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(3), pages 437-454, August.
    5. Jörg Martin & Clemens Elster, 2021. "The variation of the posterior variance and Bayesian sample size determination," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1135-1155, October.
    6. P. Brutti & F. Santis & S. Gubbiotti, 2013. "Robust Bayesian monitoring of sequential trials," METRON, Springer;Sapienza Università di Roma, vol. 71(1), pages 81-95, June.
    7. Hui Quan & Xiaofei Chen & Xun Chen & Xiaodong Luo, 2022. "Assessments of Conditional and Unconditional Type I Error Probabilities for Bayesian Hypothesis Testing with Historical Data Borrowing," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(1), pages 139-157, April.
    8. A. Gafni & S. D. Walter & S. Birch & P. Sendi, 2008. "An opportunity cost approach to sample size calculation in cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 17(1), pages 99-107, January.
    9. Wenqing Li & Ming-Hui Chen & Xiaojing Wang & Dipak K. Dey, 2018. "Bayesian Design of Non-inferiority Clinical Trials Via the Bayes Factor," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(2), pages 439-459, August.
    10. Boris G. Zaslavsky, 2013. "Bayesian Hypothesis Testing in Two-Arm Trials with Dichotomous Outcomes," Biometrics, The International Biometric Society, vol. 69(1), pages 157-163, March.
    11. Bhramar Mukherjee & Jaeil Ahn & Stephen B. Gruber & Malay Ghosh & Nilanjan Chatterjee, 2010. "Case–Control Studies of Gene–Environment Interaction: Bayesian Design and Analysis," Biometrics, The International Biometric Society, vol. 66(3), pages 934-948, September.
    12. Andrew R. Willan & Matthew E. Kowgier, 2008. "Cost‐effectiveness analysis of a multinational RCT with a binary measure of effectiveness and an interacting covariate," Health Economics, John Wiley & Sons, Ltd., vol. 17(7), pages 777-791, July.
    13. Daniel P Beavers & James D Stamey, 2018. "Bayesian sample size determination for cost-effectiveness studies with censored data," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-16, January.
    14. Alan Brennan & Samer A. Kharroubi, 2007. "Expected value of sample information for Weibull survival data," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1205-1225, November.
    15. Alan Brennan & Samer A. Kharroubi, 2007. "Expected value of sample information for Weibull survival data," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1205-1225.
    16. Fulvio De Santis, 2007. "Using historical data for Bayesian sample size determination," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 95-113, January.
    17. Thompson, Nathanael M. & Brorsen, B. Wade & DeVuyst, Eric A. & Lusk, Jayson L., 2016. "Random Sampling of Beef Cattle for Genetic Testing: Optimal Sample Size Determination," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 229195, Southern Agricultural Economics Association.
    18. Henry Glick, 2011. "Sample Size and Power for Cost-Effectiveness Analysis (Part 1)," PharmacoEconomics, Springer, vol. 29(3), pages 189-198, March.
    19. C. Armero & G. García‐Donato & A. López‐Quílez, 2010. "Bayesian methods in cost–effectiveness studies: objectivity, computation and other relevant aspects," Health Economics, John Wiley & Sons, Ltd., vol. 19(6), pages 629-643, June.
    20. Filip Melinscak & Dominik R Bach, 2020. "Computational optimization of associative learning experiments," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-23, January.

    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:stmapp:v:30:y:2021:i:2:d:10.1007_s10260-020-00539-1. 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.