IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v69y2023i6p3516-3535.html
   My bibliography  Save this article

Value-Based Clinical Trials: Selecting Recruitment Rates and Trial Lengths in Different Regulatory Contexts

Author

Listed:
  • Andres Alban

    (Technology and Operations Management, INSEAD, Fontainebleau 77300, France)

  • Stephen E. Chick

    (Technology and Operations Management, INSEAD, Fontainebleau 77300, France)

  • Martin Forster

    (Department of Statistical Sciences “Paolo Fortunati,” University of Bologna, 40126 Bologna BO, Italy; Department of Economics and Related Studies, University of York, York YO10 5DD, United Kingdom)

Abstract

Health systems are placing increasing emphasis on improving the design and operation of clinical trials with the aim of making the health technology adoption process more value-based . We present a model of a value-based, two-armed clinical trial in which both the recruitment rate and trial length are optimized. The model is value-based because it balances the cost of the trial with the expected benefit it generates for patients, valued by the relative health benefits and costs of the technologies. We consider a wide range of regulatory and practical contexts that address how patient health is valued (discount rate, time horizon, pragmatic trials). We present comparative statics and asymptotic analysis together with a retrospective application to a recent health technology assessment and an extension for adaptive trials. Results challenge traditional perceptions concerning the efficiency, length, and knowledge that may be gained from clinical research for trial managers or funders charged with delivering value efficiently: we highlight trade-offs between trial costs and population health benefits influenced by trial outcomes and the importance of optimizing both recruitment rate and trial duration rather than sample size alone.

Suggested Citation

  • Andres Alban & Stephen E. Chick & Martin Forster, 2023. "Value-Based Clinical Trials: Selecting Recruitment Rates and Trial Lengths in Different Regulatory Contexts," Management Science, INFORMS, vol. 69(6), pages 3516-3535, June.
  • Handle: RePEc:inm:ormnsc:v:69:y:2023:i:6:p:3516-3535
    DOI: 10.1287/mnsc.2022.4540
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.2022.4540
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.2022.4540?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. Simon Eckermann & Andrew R. Willan, 2007. "Expected value of information and decision making in HTA," Health Economics, John Wiley & Sons, Ltd., vol. 16(2), pages 195-209, February.
    2. DiMasi, Joseph A. & Grabowski, Henry G. & Hansen, Ronald W., 2016. "Innovation in the pharmaceutical industry: New estimates of R&D costs," Journal of Health Economics, Elsevier, vol. 47(C), pages 20-33.
    3. Karl Claxton & Larry F. Lacey & Stephen G. Walker, 2000. "Selecting treatments: a decision theoretic approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(2), pages 211-225.
    4. Susan Griffin & Nicky J. Welton & Karl Claxton, 2010. "Exploring the Research Decision Space: The Expected Value of Information for Sequential Research Designs," Medical Decision Making, , vol. 30(2), pages 155-162, March.
    5. Lisa V. Hampson & Christopher Jennison, 2013. "Group sequential tests for delayed responses (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 3-54, January.
    6. Williamson, S. Faye & Jacko, Peter & Villar, Sofía S. & Jaki, Thomas, 2017. "A Bayesian adaptive design for clinical trials in rare diseases," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 136-153.
    7. Jing Xie & Peter I. Frazier & Stephen E. Chick, 2016. "Bayesian Optimization via Simulation with Pairwise Sampling and Correlated Prior Beliefs," Operations Research, INFORMS, vol. 64(2), pages 542-559, April.
    8. Deepshikha Sharma & Arun Kumar Aggarwal & Laura E. Downey & Shankar Prinja, 2021. "National Healthcare Economic Evaluation Guidelines: A Cross-Country Comparison," PharmacoEconomics - Open, Springer, vol. 5(3), pages 349-364, September.
    9. Sofía S. Villar & William F. Rosenberger, 2018. "Covariate†adjusted response†adaptive randomization for multi†arm clinical trials using a modified forward looking Gittins index rule," Biometrics, The International Biometric Society, vol. 74(1), pages 49-57, March.
    10. Claxton, Karl, 1999. "The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies," Journal of Health Economics, Elsevier, vol. 18(3), pages 341-364, June.
    11. Paolo Pertile & Martin Forster & Davide La Torre, 2014. "Optimal Bayesian sequential sampling rules for the economic evaluation of health technologies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(2), pages 419-438, February.
    12. Jobjörnsson, Sebastian & Forster, Martin & Pertile, Paolo & Burman, Carl-Fredrik, 2016. "Late-stage pharmaceutical R&D and pricing policies under two-stage regulation," Journal of Health Economics, Elsevier, vol. 50(C), pages 298-311.
    13. Ahuja, Vishal & Birge, John R., 2016. "Response-adaptive designs for clinical trials: Simultaneous learning from multiple patients," European Journal of Operational Research, Elsevier, vol. 248(2), pages 619-633.
    14. repec:dau:papers:123456789/1908 is not listed on IDEAS
    15. Stephen Chick & Martin Forster & Paolo Pertile, 2017. "A Bayesian decision theoretic model of sequential experimentation with delayed response," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1439-1462, November.
    16. Stephen E. Chick & Yaozhong Wu, 2005. "Selection Procedures with Frequentist Expected Opportunity Cost Bounds," Operations Research, INFORMS, vol. 53(5), pages 867-878, October.
    17. Elisa F. Long & Naveen K. Vaidya & Margaret L. Brandeau, 2008. "Controlling Co-Epidemics: Analysis of HIV and Tuberculosis Infection Dynamics," Operations Research, INFORMS, vol. 56(6), pages 1366-1381, December.
    18. Karl Claxton & John Posnett, "undated". "An Economic Approach to Clinical Trial Design and Research Priority Setting," Discussion Papers 96/19, Department of Economics, University of York.
    19. Ilya O. Ryzhov & Warren B. Powell & Peter I. Frazier, 2012. "The Knowledge Gradient Algorithm for a General Class of Online Learning Problems," Operations Research, INFORMS, vol. 60(1), pages 180-195, February.
    20. Panos Kouvelis & Joseph Milner & Zhili Tian, 2017. "Clinical Trials for New Drug Development: Optimal Investment and Application," Manufacturing & Service Operations Management, INFORMS, vol. 19(3), pages 437-452, July.
    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. Stephen E. Chick & Noah Gans & Özge Yapar, 2022. "Bayesian Sequential Learning for Clinical Trials of Multiple Correlated Medical Interventions," Management Science, INFORMS, vol. 68(7), pages 4919-4938, July.
    2. Williamson, S. Faye & Jacko, Peter & Jaki, Thomas, 2022. "Generalisations of a Bayesian decision-theoretic randomisation procedure and the impact of delayed responses," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    3. Arielle Anderer & Hamsa Bastani & John Silberholz, 2022. "Adaptive Clinical Trial Designs with Surrogates: When Should We Bother?," Management Science, INFORMS, vol. 68(3), pages 1982-2002, March.
    4. Andrew Willan & Simon Eckermann, 2012. "Value of Information and Pricing New Healthcare Interventions," PharmacoEconomics, Springer, vol. 30(6), pages 447-459, June.
    5. Panos Kouvelis & Joseph Milner & Zhili Tian, 2017. "Clinical Trials for New Drug Development: Optimal Investment and Application," Manufacturing & Service Operations Management, INFORMS, vol. 19(3), pages 437-452, July.
    6. Samer A. Kharroubi & Alan Brennan & Mark Strong, 2011. "Estimating Expected Value of Sample Information for Incomplete Data Models Using Bayesian Approximation," Medical Decision Making, , vol. 31(6), pages 839-852, November.
    7. Stephen Chick & Martin Forster & Paolo Pertile, 2017. "A Bayesian decision theoretic model of sequential experimentation with delayed response," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1439-1462, November.
    8. Andrew R. Willan & Simon Eckermann, 2010. "Optimal clinical trial design using value of information methods with imperfect implementation," Health Economics, John Wiley & Sons, Ltd., vol. 19(5), pages 549-561, May.
    9. Vishal Ahuja & John R. Birge, 2020. "An Approximation Approach for Response-Adaptive Clinical Trial Design," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 877-894, October.
    10. Amir Ali Nasrollahzadeh & Amin Khademi, 2022. "Dynamic Programming for Response-Adaptive Dose-Finding Clinical Trials," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 1176-1190, March.
    11. Simon Eckermann & Andrew R. Willan, 2009. "Globally optimal trial design for local decision making," Health Economics, John Wiley & Sons, Ltd., vol. 18(2), pages 203-216, February.
    12. N. J. Welton & A. E. Ades & D. M. Caldwell & T. J. Peters, 2008. "Research prioritization based on expected value of partial perfect information: a case‐study on interventions to increase uptake of breast cancer screening," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(4), pages 807-841, October.
    13. Haitham Tuffaha & Shelley Roberts & Wendy Chaboyer & Louisa Gordon & Paul Scuffham, 2015. "Cost-Effectiveness and Value of Information Analysis of Nutritional Support for Preventing Pressure Ulcers in High-risk Patients: Implement Now, Research Later," Applied Health Economics and Health Policy, Springer, vol. 13(2), pages 167-179, April.
    14. Andrew R. Willan & Simon Eckermann, 2012. "Accounting For Between‐Study Variation In Incremental Net Benefit In Value Of Information Methodology," Health Economics, John Wiley & Sons, Ltd., vol. 21(10), pages 1183-1195, October.
    15. Andrew Willan, 2011. "Sample Size Determination for Cost-Effectiveness Trials," PharmacoEconomics, Springer, vol. 29(11), pages 933-949, November.
    16. Thijssen, Jacco J.J. & Bregantini, Daniele, 2017. "Costly sequential experimentation and project valuation with an application to health technology assessment," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 202-229.
    17. Gamba, Simona & Magazzini, Laura & Pertile, Paolo, 2021. "R&D and market size: Who benefits from orphan drug legislation?," Journal of Health Economics, Elsevier, vol. 80(C).
    18. Qi Cao & Erik Buskens & Hans L. Hillege & Tiny Jaarsma & Maarten Postma & Douwe Postmus, 2019. "Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(3), pages 475-482, April.
    19. Seokhyun Chung & Raed Al Kontar & Zhenke Wu, 2022. "Weakly Supervised Multi-output Regression via Correlated Gaussian Processes," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 115-137, October.
    20. Helen Yvette Barnett & Sofía S. Villar & Helena Geys & Thomas Jaki, 2023. "A novel statistical test for treatment differences in clinical trials using a response‐adaptive forward‐looking Gittins Index Rule," Biometrics, The International Biometric Society, vol. 79(1), pages 86-97, March.

    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:inm:ormnsc:v:69:y:2023:i:6:p:3516-3535. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.