Expected Value of Sample Information to Guide the Design of Group Sequential Clinical Trials

1. National Institute of Health Research . Annual efficient studies funding calls for CTU projects. 2019. Available from: https://www.nihr.ac.uk/documents/ad-hoc-funding-calls-for-ctu-projects/20141
Google Scholar2. Adaptive designs in clinical drug development—an executive summary of the phrma working group . J Biopharm Stat. 2006;16(3):275–83.
Google Scholar3. Bretz, F, Koenig, F, Brannath, W, Glimm, E, Posch, M. Adaptive designs for confirmatory clinical trials. Stat Med. 2009;28(8):1181–217.
Google Scholar | Crossref4. Pallmann, P, Bedding, AW, Choodari-Oskooei, B, et al. Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Med. 2018;16(1):29.
Google Scholar | Crossref | Medline5. Hatfield, I, Allison, A, Flight, L, Julious, SA, Dimairo, M. Adaptive designs undertaken in clinical research: a review of registered clinical trials. Trials. 2016;17(1):150.
Google Scholar | Crossref | Medline6. Mistry, P, Dunn, JA, Marshall, A. A literature review of applied adaptive design methodology within the field of oncology in randomised controlled trials and a proposed extension to the consort guidelines. BMC Med Res Methodol. 2017;17(1):108.
Google Scholar | Crossref | Medline7. Bothwell, L, Avron, J, Khan, N, Kesselheim, A. Adaptive design clinical trials: a review of the literature and clinicaltrials.gov. BMJ Open. 2018;8(2):e018320.
Google Scholar | Crossref | Medline8. RECOVERY Collaborative Group . Dexamethasone in hospitalized patients with Covid-19—preliminary report. N Engl J Med. 2021;384(8):693–704.
Google Scholar | Crossref | Medline9. Flight, L, Arshad, F, Barnsley, R, et al. A review of clinical trials with an adaptive design and health economic analysis. Value Health. 2019;22(4):391–8.
Google Scholar | Crossref | Medline10. Rothery, C, Strong, M, Koffijberg, HE, et al. Value of information analytical methods: report 2 of the ISPOR value of information analysis emerging good practices task force. Value Health. 2020;23(3):277–86.
Google Scholar | Crossref11. Flight, L, Julious, SA, Brennan, A, Todd, S, Hind, D. How can health economics be used in the design and analysis of adaptive clinical trials? A qualitative analysis. Trials. 2020;21(1):1–12.
Google Scholar | Crossref | Medline12. Fenwick, E, Steuten, L, Knies, S, et al. Value of information analysis for research decisions—an introduction: report 1 of the ISPOR Value of Information Analysis Emerging Good Practices Task Force. Value Health. 2020;23(2):139–50.
Google Scholar | Crossref13. Palmer, R, Enderby, P, Cooper, C, et al. Computer therapy compared with usual care for people with long-standing aphasia poststroke: a pilot randomized controlled trial. Stroke. 2012;43(7):1904–11.
Google Scholar | Crossref | Medline14. Whitehead, J . The Design and Analysis of Sequential Clinical Trials. New York: Wiley; 1997.
Google Scholar | Crossref15. Pocock, SJ . Group sequential methods in the design and analysis of clinical trials. Biometrika. 1977;64:191–9.
Google Scholar | Crossref16. O’Brien, PC, Fleming, TR. A multiple testing procedure for clinical trials. Biometrics. 1979;35:549–56.
Google Scholar | Crossref | Medline17. Jennison, C, Turnbull, BW. Group Sequential Methods with Applications to Clinical Trials. London: Chapman and Hall/CRC; 2000.
Google Scholar18. Flight, L . The Use of Health Economics in the Design and Analysis of Adaptive Clinical Trials. Sheffield (UK): University of Sheffield; 2020.
Google Scholar19. Ades, AE, Lu, G, Claxton, K. Expected value of sample information calculations in medical decision modelling. Med Decis Making. 2004;24:207–27.
Google Scholar | SAGE Journals20. Welton, NJ, Ades, AE, Caldwell, DM, Peters, TJ. Research prioritization based on expected value of partial perfect information: a case-study on interventions to increase uptake of breast cancer screening. J R Stat Soc Ser A Stat Soc. 2008;171(4):807–34.
Google Scholar | Crossref21. Griffin, S, Welton, NJ, Claxton, K. Exploring the research design space: the expected value of information for sequential research designs. Med Decis Making. 2010;30:155.
Google Scholar | SAGE Journals | ISI22. Boyd, KA, Fenwick, E, Briggs, A. Using an iterative approach to economic evaluation in the drug development process. Drug Dev Res. 2010;71(8):470–7.
Google Scholar | Crossref23. Flight, L, Julious, SA. Practical guide to sample size calculations: an introduction. Pharm Stat. 2016;15(1):68–74.
Google Scholar | Crossref | Medline24. Whitehead, J . On the bias of maximum likelihood estimation following a sequential test. Biometrika. 1986;73(3):573–81.
Google Scholar | Crossref25. Whitehead, J . Supplementary analysis at the conclusion of a sequential clinical trial. Biometrics. 1986;461–71.
Google Scholar | Crossref | Medline26. Emerson, SS, Fleming, TR. Parameter estimation following group sequential hypothesis testing. Biometrika. 1990;77(4):875–92.
Google Scholar | Crossref27. Strong, M, Oakley, JE, Brennan, A, Breeze, P. Estimating the expected value of sample information using the probabilistic sensitivity analysis sample: a fast, nonparametric regression-based method. Med Decis Making. 2015;35(5):570–83.
Google Scholar | SAGE Journals28. Welton, NJ, Madan, JJ, Caldwell, DM, Peters, TJ, Ades, a E. Expected value of sample information for multi-arm cluster randomized trials with binary outcomes. Med Decis Making. 2013;34(April):352–65.
Google Scholar | Medline29. Heath, A, Kunst, NR, Jackson, C, et al. Calculating the expected value of sample information in practice: considerations from three case studies. Med Decis Making. 2020;40(3).
Google Scholar | SAGE Journals30. Kunst, NR, Wilson, E, Alarid-Escudero, F, et al. Computing the expected value of sample information efficiently: expertise and skills required for four model-based methods. Value Health. 2020;23(6):734–42.
Google Scholar | Crossref | Medline31. Eckermann, SD, Willan, AR. Globally optimal trial design for local decision making. Health Econ. 2009;18:203–16.
Google Scholar | Crossref | Medline32. Zhao, G, Chen, W. Enhancing R&D in science-based industry: an optimal stopping model for drug discovery. Int J Proj Manag. 2009;27(8):754–64.
Google Scholar | Crossref33. Willan, AR, Kowgier, M. Determining optimal sample sizes for multi-stage randomized clinical trials using value of information methods. Clin Trials. 2008;5:289–300.
Google Scholar | SAGE Journals | ISI34. Latimer, NR, Dixon, S, Palmer, R. Cost-utility of self-managed computer therapy for people with aphasia. Int J Technol Assess Health Care. 2013;29(4):402–9.
Google Scholar | Crossref | Medline35. Palmer, R, Dimairo, M, Latimer, N, et al. Computerised speech and language therapy or attention control added to usual care for people with long-term post-stroke aphasia: the Big CACTUS three-arm RCT. Health Technol Assess. 2020;24(19):1.
Google Scholar | Crossref36. National Institute for Health and Care Excellence . Guide to the methods of technology appraisal. 2013. Available from: http://www.nice.org.uk/article/pmg9/chapter/foreword
Google Scholar37. Berry, DA, Ho, C-H. One-sided sequential stopping boundaries for clinical trials: a decision-theoretic approach. Biometrics. 1988;44(1):219–27.
Google Scholar | Crossref | Medline38. Pertile, P, Forster, M, Torre, D La. Optimal Bayesian sequential sampling rules for the economic evaluation of health technologies. J R Stat Soc Ser A Stat Soc. 2014;177(2):419–38.
Google Scholar | Crossref39. Chick, S, Forster, M, Pertile, P. A Bayesian decision theoretic model of sequential experimentation with delayed response. J R Stat Soc Ser B (Stat Methodol). 2017;79(5):1439–62.
Google Scholar | Crossref40. EcoNomics of Adaptive Clinical Trials (ENACT) . Value-adaptive clinical trial designs for efficient delivery of NIHR research. 2021 Available from: https://www.sheffield.ac.uk/scharr/research/centres/ctru/enact
Google Scholar41. Alban, A, Chick, S, Forster, M. Value-based clinical trials: selecting trial lengths and recruitment rates in different regulatory contexts. Discussion Paper 20/01, Department of Economics, University of York. 2020; Available from: https://ideas.repec.org/p/yor/yorken/20-01.html
Google Scholar | Crossref42. Flight, L, Brennan, A, Chick, S, Forster, M, Julious, SA, Tharmanathan, P. Value-adaptive clinical trial designs for efficient delivery of research—actions, opportunities and challenges for publicly funded trials. 2021. In press.
Google Scholar43. Forster, M, Flight, L, Corbacho, B, et al. Application of a Bayesian value-based sequential model of a clinical trial to the HERO and CACTUS case studies. 2021. In press.
Google Scholar44. Dimairo, M, Pallmann, P, Wason, J, et al. The Adaptive designs CONSORT Extension (ACE) Statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design. BMJ. 2020;17:369.
Google Scholar45. US Food and Drug Administration. Guidance for industry: adaptive design clinical trials for drugs and biologics . 2019. Available from: http://www.fda.gov/downloads/Drugs/Guidances/ucm201790.pdf
Google Scholar46. Claxton, K, Palmer, S, Longworth, L. Informing a decision framework for when NICE should recommend the use of health technologies only in the context of an appropriately designed programme of evidence development. Health Technol Assess. 2012;16(46):1–323.
Google Scholar | Crossref | Medline | ISI47. Heath, A, Manolopoulou, I, Baio, G. Efficient Monte Carlo estimation of the expected value of sample information using moment matching. Med Decis Making. 2018;38(2):163–73.
Google Scholar | SAGE Journals48. Menzies, NA . An efficient estimator for the expected value of sample information. Med Decis Making. 2016;36(3):308–20.
Google Scholar | SAGE Journals49. Jalal, H, Goldhaber-Fiebert, JD, Kuntz, KM. Computing expected value of partial sample information from probabilistic sensitivity analysis using linear regression metamodeling. Med Decis Making. 2015;35(5):584–95.
Google Scholar | SAGE Journals50. Jalal, H, Alarid-Escudero, F. A Gaussian approximation approach for value of information analysis. Med Decis Making. 2018;38(2):174–88.
Google Scholar | SAGE Journals51. Ward, M, Grayling, M, Wason, J, et al. PSU4 VALUE of information for adaptive trials: proof of concept study in MULTI-arm MULTI-STAGE trials of interventions for the prevention of surgical site infections. Value Health. 2020;23(suppl 2):S738.
Google Scholar | Crossref

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