IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v34y2014i3p352-365.html
   My bibliography  Save this article

Expected Value of Sample Information for Multi-Arm Cluster Randomized Trials with Binary Outcomes

Author

Listed:
  • Nicky J. Welton
  • Jason J. Madan
  • Deborah M. Caldwell
  • Tim J. Peters
  • Anthony E. Ades

Abstract

Expected value of sample information (EVSI) measures the anticipated net benefit gained from conducting new research with a specific design to add to the evidence on which reimbursement decisions are made. Cluster randomized trials raise specific issues for EVSI calculations because 1) a hierarchical model is necessary to account for between-cluster variability when incorporating new evidence and 2) heterogeneity between clusters needs to be carefully characterized in the cost-effectiveness analysis model. Multi-arm trials provide parameter estimates that are correlated, which needs to be accounted for in EVSI calculations. Furthermore, EVSI is computationally intensive when the net benefit function is nonlinear, due to the need for an inner-simulation step. We develop a method for the computation of EVSI that avoids the inner simulation step for cluster randomized multi-arm trials with a binary outcome, where the net benefit function is linear in the probability of an event but nonlinear in the log-odds ratio parameters. We motivate and illustrate the method with an example of a cluster randomized 2 × 2 factorial trial for interventions to increase attendance at breast screening in the UK, using a previously reported cost-effectiveness model. We highlight assumptions made in our approach, extensions to individually randomized trials and inclusion of covariates, and areas for further developments. We discuss computation time, the research-design space, and the ethical implications of an EVSI approach. We suggest that EVSI is a practical and appropriate tool for the design of cluster randomized trials.

Suggested Citation

  • Nicky J. Welton & Jason J. Madan & Deborah M. Caldwell & Tim J. Peters & Anthony E. Ades, 2014. "Expected Value of Sample Information for Multi-Arm Cluster Randomized Trials with Binary Outcomes," Medical Decision Making, , vol. 34(3), pages 352-365, April.
    • Handle: RePEc:sae:medema:v:34:y:2014:i:3:p:352-365
    DOI: 10.1177/0272989X13501229
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X13501229
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X13501229?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. Oakley, Jeremy E. & Brennan, Alan & Tappenden, Paul & Chilcott, Jim, 2010. "Simulation sample sizes for Monte Carlo partial EVPI calculations," Journal of Health Economics, Elsevier, vol. 29(3), pages 468-477, May.
    2. 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.
    3. 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.
    4. Nicky Welton & A. E. Ades, 2012. "Research Decisions In The Face Of Heterogeneity: What Can A New Study Tell Us?," Health Economics, John Wiley & Sons, Ltd., vol. 21(10), pages 1196-1200, October.
    5. J. Brown & N. J. Welton & C. Bankhead & S. H. Richards & L. Roberts & C. Tydeman & T. J. Peters, 2006. "A Bayesian approach to analysing the cost‐effectiveness of two primary care interventions aimed at improving attendance for breast screening," Health Economics, John Wiley & Sons, Ltd., vol. 15(5), pages 435-445, May.
    6. 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.
    7. 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.
    8. Karl Claxton & Mark Sculpher & Chris McCabe & Andrew Briggs & Ron Akehurst & Martin Buxton & John Brazier & Tony O'Hagan, 2005. "Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 339-347, April.
    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. Sofia Dias & Alex J. Sutton & Nicky J. Welton & A. E. Ades, 2013. "Evidence Synthesis for Decision Making 6," Medical Decision Making, , vol. 33(5), pages 671-678, July.
    2. 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.
    3. A. E. Ades & Karl Claxton & Mark Sculpher, 2006. "Evidence synthesis, parameter correlation and probabilistic sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 373-381, April.
    4. Isaac Corro Ramos & Maureen P. M. H. Rutten-van Mölken & Maiwenn J. Al, 2013. "The Role of Value-of-Information Analysis in a Health Care Research Priority Setting," Medical Decision Making, , vol. 33(4), pages 472-489, May.
    5. Neil Hawkins & Mark Sculpher & David Epstein, 2005. "Cost-Effectiveness Analysis of Treatments for Chronic Disease: Using R to Incorporate Time Dependency of Treatment Response," Medical Decision Making, , vol. 25(5), pages 511-519, September.
    6. John Hutton, 2012. "‘Health Economics’ and the evolution of economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 21(1), pages 13-18, January.
    7. Mark Strong & Jeremy E. Oakley, 2013. "An Efficient Method for Computing Single-Parameter Partial Expected Value of Perfect Information," Medical Decision Making, , vol. 33(6), pages 755-766, August.
    8. 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.
    9. 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.
    10. Mark Strong & Jeremy E. Oakley & Alan Brennan & Penny Breeze, 2015. "Estimating the Expected Value of Sample Information Using the Probabilistic Sensitivity Analysis Sample," Medical Decision Making, , vol. 35(5), pages 570-583, July.
    11. Josh J. Carlson & Rahber Thariani & Josh Roth & Julie Gralow & N. Lynn Henry & Laura Esmail & Pat Deverka & Scott D. Ramsey & Laurence Baker & David L. Veenstra, 2013. "Value-of-Information Analysis within a Stakeholder-Driven Research Prioritization Process in a US Setting: An Application in Cancer Genomics," Medical Decision Making, , vol. 33(4), pages 463-471, May.
    12. Mark Strong & Jeremy E. Oakley & Alan Brennan, 2014. "Estimating Multiparameter Partial Expected Value of Perfect Information from a Probabilistic Sensitivity Analysis Sample," Medical Decision Making, , vol. 34(3), pages 311-326, April.
    13. Jeff Miller & Rolf Ulrich, 2019. "The quest for an optimal alpha," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-13, January.
    14. Hawre Jalal & Jeremy D. Goldhaber-Fiebert & Karen M. Kuntz, 2015. "Computing Expected Value of Partial Sample Information from Probabilistic Sensitivity Analysis Using Linear Regression Metamodeling," Medical Decision Making, , vol. 35(5), pages 584-595, July.
    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, November.
    16. Jennifer Uyei & R. Scott Braithwaite, 2016. "Are There Scenarios When the Use of Non–Placebo-Control Groups in Experimental Trial Designs Increase Expected Value to Society?," Medical Decision Making, , vol. 36(1), pages 20-30, January.
    17. Manuel A. Espinoza & Andrea Manca & Karl Claxton & Mark J. Sculpher, 2014. "The Value of Heterogeneity for Cost-Effectiveness Subgroup Analysis," Medical Decision Making, , vol. 34(8), pages 951-964, November.
    18. Anirban Basu & David Meltzer, 2018. "Decision Criterion and Value of Information Analysis: Optimal Aspirin Dosage for Secondary Prevention of Cardiovascular Events," Medical Decision Making, , vol. 38(4), pages 427-438, May.
    19. Franck Maunoury & Anastasiia Motrunich & Maria Palka-Santini & Stéphanie F Bernatchez & Stéphane Ruckly & Jean-François Timsit, 2015. "Cost-Effectiveness Analysis of a Transparent Antimicrobial Dressing for Managing Central Venous and Arterial Catheters in Intensive Care Units," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-14, June.
    20. Sze Huey Tan & Keith R. Abrams & Sylwia Bujkiewicz, 2018. "Bayesian Multiparameter Evidence Synthesis to Inform Decision Making: A Case Study in Metastatic Hormone-Refractory Prostate Cancer," Medical Decision Making, , vol. 38(7), pages 834-848, October.

    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:34:y:2014:i:3:p:352-365. 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.