IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v157y1994i3p357-387.html
   My bibliography  Save this article

Bayesian Approaches to Randomized Trials

Author

Listed:
  • David J. Spiegelhalter
  • Laurence S. Freedman
  • Mahesh K. B. Parmar

Abstract

Statistical issues in conducting randomized trials include the choice of a sample size, whether to stop a trial early and the appropriate analysis and interpretation of the trial results. At each of these stages, evidence external to the trial is useful, but generally such evidence is introduced in an unstructured and informal manner. We argue that a Bayesian approach allows a formal basis for using external evidence and in addition provides a rational way for dealing with issues such as the ethics of randomization, trials to show treatment equivalence, the monitoring of accumulating data and the prediction of the consequences of continuing a study. The motivation for using this methodology is practical rather than ideological.

Suggested Citation

  • David J. Spiegelhalter & Laurence S. Freedman & Mahesh K. B. Parmar, 1994. "Bayesian Approaches to Randomized Trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(3), pages 357-387, May.
    • Handle: RePEc:bla:jorssa:v:157:y:1994:i:3:p:357-387
    DOI: 10.2307/2983527
    as

    Download full text from publisher

    File URL: https://doi.org/10.2307/2983527
    Download Restriction: no
    File URL: https://libkey.io/10.2307/2983527?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
    ---><---

    Citations

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


    Cited by:

    1. Charles F. Manski, 2018. "Reasonable patient care under uncertainty," Health Economics, John Wiley & Sons, Ltd., vol. 27(10), pages 1397-1421, October.
    2. Charles F. Manski, 2017. "Improving Clinical Guidelines and Decisions under Uncertainty," NBER Working Papers 23915, National Bureau of Economic Research, Inc.
    3. David M. Rindskopf & William R. Shadish & M. H. Clark, 2018. "Using Bayesian Correspondence Criteria to Compare Results From a Randomized Experiment and a Quasi-Experiment Allowing Self-Selection," Evaluation Review, , vol. 42(2), pages 248-280, April.
    4. Martin E. Backhouse, 1998. "An investment appraisal approach to clinical trial design," Health Economics, John Wiley & Sons, Ltd., vol. 7(7), pages 605-619, November.
    5. Poitevineau, Jacques & Lecoutre, Bruno, 2010. "Implementing Bayesian predictive procedures: The K-prime and K-square distributions," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 724-731, March.
    6. Charles F. Manski, 2021. "Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald," Econometrica, Econometric Society, vol. 89(6), pages 2827-2853, November.
    7. Norman Simón Rodríguez Cano, 2018. "Tendencias actuales en la evaluación de políticas públicas," Ensayos de Economía 17296, Universidad Nacional de Colombia Sede Medellín.
    8. Charles F. Manski, 2019. "Meta-Analysis for Medical Decisions," NBER Working Papers 25504, National Bureau of Economic Research, Inc.
    9. James C. Felli & Gordon B. Hazen, 1998. "Sensitivity Analysis and the Expected Value of Perfect Information," Medical Decision Making, , vol. 18(1), pages 95-109, January.
    10. Leonhard Held, 2020. "A new standard for the analysis and design of replication studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 431-448, February.
    11. Francisco-José Polo & Miguel Negrín & Xavier Badía & Montse Roset, 2005. "Bayesian regression models for cost-effectiveness analysis," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 6(1), pages 45-52, March.
    12. Darren Bernard & Nicole L. Cade & Frank Hodge, 2018. "Investor Behavior and the Benefits of Direct Stock Ownership," Journal of Accounting Research, Wiley Blackwell, vol. 56(2), pages 431-466, May.
    13. Kruschke, John K. & Liddell, Torrin, 2016. "The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective," OSF Preprints ksfyr, Center for Open Science.
    14. Charles F. Manski & Aleksey Tetenov, 2015. "Clinical trial design enabling ε-optimal treatment rules," CeMMAP working papers 60/15, Institute for Fiscal Studies.
    15. Isakov, Leah & Lo, Andrew W. & Montazerhodjat, Vahid, 2019. "Is the FDA too conservative or too aggressive?: A Bayesian decision analysis of clinical trial design," Journal of Econometrics, Elsevier, vol. 211(1), pages 117-136.
    16. Nandini Dendukuri & Lawrence Joseph, 2001. "Bayesian Approaches to Modeling the Conditional Dependence Between Multiple Diagnostic Tests," Biometrics, The International Biometric Society, vol. 57(1), pages 158-167, March.
    17. Francesco De Pretis & Barbara Osimani, 2019. "New Insights in Computational Methods for Pharmacovigilance: E-Synthesis , a Bayesian Framework for Causal Assessment," IJERPH, MDPI, vol. 16(12), pages 1-19, June.
    18. Jingjing Ye & Gregory Reaman, 2022. "Improving Early Futility Determination by Learning from External Data in Pediatric Cancer Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(2), pages 337-351, July.
    19. Constantin Volkmann & Alexander Volkmann & Christian A Müller, 2020. "On the treatment effect heterogeneity of antidepressants in major depression: A Bayesian meta-analysis and simulation study," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-22, November.
    20. Paul Gustafson & Nhu D. Le & Refik Saskin, 2001. "Case–Control Analysis with Partial Knowledge of Exposure Misclassification Probabilities," Biometrics, The International Biometric Society, vol. 57(2), pages 598-609, June.
    21. Bradley P. Carlin & James S. Hodges, 1999. "Hierarchical Proportional Hazards Regression Models for Highly Stratified Data," Biometrics, The International Biometric Society, vol. 55(4), pages 1162-1170, December.
    22. Peter F. Thall & Richard M. Simon & Yu Shen, 2000. "Approximate Bayesian Evaluation of Multiple Treatment Effects," Biometrics, The International Biometric Society, vol. 56(1), pages 213-219, March.
    23. Karl Claxton & John Posnett, 1996. "An economic approach to clinical trial design and research priority‐setting," Health Economics, John Wiley & Sons, Ltd., vol. 5(6), pages 513-524, November.
    24. Danila Azzolina & Giulia Lorenzoni & Silvia Bressan & Liviana Da Dalt & Ileana Baldi & Dario Gregori, 2021. "Handling Poor Accrual in Pediatric Trials: A Simulation Study Using a Bayesian Approach," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
    25. Miguel A. Negrín & Francisco J. Vázquez-Polo & María Martel & Elías Moreno & Francisco J. Girón, 2010. "Bayesian Variable Selection in Cost-Effectiveness Analysis," IJERPH, MDPI, vol. 7(4), pages 1-20, April.

    More about this item

    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:bla:jorssa:v:157:y:1994:i:3:p:357-387. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.