An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial

1. Schlaifer, R . Probability and Statistics for Business Decisions. 1st ed. New York: McGraw-Hill; 1959.
Google Scholar2. Raiffa, H, Schlaifer, R. Applied Statistical Decision Theory. Boston: Division of Research, Graduate School of Business Adminitration, Harvard University; 1961.
Google Scholar3. 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 | Medline4. European Medicines Agency . Adaptive pathways. 2018. Available from: https://www.ema.europa.eu/en/human-regulatory/research-development/adaptive-pathways. Accessed October 19, 2020.
Google Scholar5. European Medicines Agency . Conditional marketing authorisation. 2018. Available from: https://www.ema.europa.eu/en/human-regulatory/marketing-authorisation/conditional-marketing-authorisation. Accessed October 19, 2020.
Google Scholar6. Claxton, K, Palmer, S, Longworth, L, et al. A comprehensive algorithm for approval of health technologies with, without, or only in research: the key principles for informing coverage decisions. Value Health. 2016;19(6):885–91.
Google Scholar | Crossref7. Eckermann, S, Willan, AR. The option value of delay in health technology assessment. Med Decis Making. 2008;28(3):300–5.
Google Scholar | SAGE Journals8. Gallacher, D, Kimani, P, Stallard, N. Extrapolating parametric survival models in health technology assessment: a simulation study. Med Decis Making. 2021;41(1):37–50.
Google Scholar | SAGE Journals | ISI9. Gallacher, D, Kimani, P, Stallard, N. Extrapolating parametric survival models in health technology assessment using model averaging: a simulation study. Med Decis Making. 2021;41(4):476–84.
Google Scholar | SAGE Journals10. 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 Journals11. Heath, A, Kunst, N, Jackson, C, et al. Calculating the expected value of sample information in practice: considerations from 3 case studies. Med Decis Making. 2020;40(3):314–26.
Google Scholar | SAGE Journals12. Ades, AE, Lu, G, Claxton, K. Expected value of sample information calculations in medical decision modeling. Med Decis Making. 2004;24(2):207–27.
Google Scholar | SAGE Journals13. Albert, J . Bayesian Computation with R.2. Dordrecht (the Netherlands): Springer; 2009.
Google Scholar | Crossref14. Latimer, NR . Survival analysis for economic evaluations alongside clinical trials—extrapolation with patient-level data: inconsistencies, limitations, and a practical guide. Med Decis Making. 2013;33(6):743–54.
Google Scholar | SAGE Journals15. Collett, D . Modelling Survival Data in Medical Research. 3rd ed. London: Chapman and Hall/CRC; 2015.
Google Scholar | Crossref16. Klein, JP, Moeschberger, ML. Survival Analysis: Techniques for Censored and Truncated Data. New York: Springer Science & Business Media; 2013.
Google Scholar17. Bernardo, JM, Smith, AFM. Bayesian Theory. Chichester (UK): Wiley; 1994.
Google Scholar | Crossref18. Jackson, CH, Thompson, SG, Sharples, LD. Accounting for uncertainty in health economic decision models by using model averaging. J R Stat Soc Ser A Stat Soc. 2009;172(2):383–404.
Google Scholar | Crossref | Medline | ISI19. Jackson, CH, Sharples, LD, Thompson, SG. Structural and parameter uncertainty in Bayesian cost-effectiveness models. J R Stat Soc Ser C Appl Stat. 2010;59(2):233–53.
Google Scholar | Crossref | Medline20. Akaike, H . Information theory and an extension of the maximum likelihood principle. In: Petrov, BN, Csaki, F, eds. Proceedings of the 2nd International Symposium on Information Theory. Budapest: Akademiai Kiado; 1973. p 267–81.
Google Scholar21. Meng, X-L, Wong, WH. Simulating ratios of normalizing constants via a simple identity: a theoretical exploration. Stat Sin. 1996;6(4):831–60.
Google Scholar22. Frühwirth-Schnatter, S . Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques. Econ J. 2004;7(1):143–67.
Google Scholar23. Gronau, QF, Sarafoglou, A, Matzke, D, et al. A tutorial on bridge sampling. J Math Psychol. 2017;81:80–97.
Google Scholar | Crossref | Medline24. Wong, JST, Forster, JJ, Smith, PWF. Properties of the bridge sampler with a focus on splitting the MCMC sample. Stat Comput. 2020;30(4):799–816.
Google Scholar | Crossref25. Gronau, QF, Singmann, H, Wagenmakers, E-J. Bridgesampling: an R package for estimating normalizing constants. J Stat Softw. 2020;92(1):1–29.
Google Scholar26. Jackson, C . Flexsurv: a platform for parametric survival modeling in R. J Stat Softw. 2016;70(1):1–33.
Google Scholar27. Guo, J, Gabry, J, Goodrich, B, Weber, S. Rstan: R interface to stan. 2020. Available from: https://CRAN.R-project.org/package=rstan
Google Scholar28. Gelman, A, Lee, D, Guo, J. Stan: a probabilistic programming language for bayesian inference and optimization. J Educ Behav Stat. 2015;40(5):530–43.
Google Scholar | SAGE Journals29. Lunn, D, Spiegelhalter, D, Thomas, A, Best, N. The BUGS project: evolution, critique and future directions. Stat Med. 2009;28(25):3049–67.
Google Scholar30. Wood, S. Mgcv: mixed GAM computation vehicle with automatic smoothness estimation. 2020. Available from: https://CRAN.R-project.org/package=mgcv
Google Scholar31. Strong, M, Oakley, JE, Brennan, A. Estimating multiparameter partial expected value of perfect information from a probabilistic sensitivity analysis sample: a nonparametric regression approach. Med Decis Making. 2014;34(3):311–26.
Google Scholar | SAGE Journals32. Claxton, K, Posnett, J. An economic approach to clinical trial design and research priority-setting. Health Econ. 1996;5(6):513–24.
Google Scholar | Crossref | Medline33. McKenna, C, Soares, M, Claxton, K, et al. Unifying research and reimbursement decisions: case studies demonstrating the sequence of assessment and judgments required. Value Health. 2015;18(6):865–75.
Google Scholar | Crossref | Medline34. Briggs, A, Claxton, K, Sculpher, M. Decision Modelling for Health Economic Evaluation. 1st ed. Oxford (UK): Oxford University Press; 2006.
Google Scholar35. Jackson, CH, Bojke, L, Thompson, SG, Claxton, K, Sharples, LD. A framework for addressing structural uncertainty in decision models. Med Decis Making. 2011;31(4):662–74.
Google Scholar | SAGE Journals36. Cox, C . The generalized F distribution: an umbrella for parametric survival analysis. Stat Med. 2008;27(21):4301–12.
Google Scholar | Crossref | Medline37. Eckermann, S . Health Economics from Theory to Practice. Cham (UK): Springer International Publishing; 2017.
Google Scholar | Crossref38. Jackson, C, Stevens, J, Ren, S, et al. Extrapolating survival from randomized trials using external data: a review of methods. Med Decis Making. 2017;37(4):377–90.
Google Scholar | SAGE Journals39. O’Hagan, A . Expert knowledge elicitation: subjective but scientific. Am Stat. 2019;73(suppl 1):69–81.
Google Scholar | Crossref40. Royston, P, Parmar, MKB. Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med. 2002;21(15):2175–97.
Google Scholar | Crossref41. Angelis, RD, Capocaccia, R, Hakulinen, T, Soderman, B, Verdecchia, A. Mixture models for cancer survival analysis: application to population-based data with covariates. Stat Med. 1999;18(4):441–54.
Google Scholar | Crossref | Medline42. Armitage, P . The search for optimality in clinical trials. Int Stat Rev. 1985;53(1):15–24.
Google Scholar | Crossref43. Walton, MJ, O’Connor, J, Carroll, C, Claxton, L, Hodgson, R. A review of issues affecting the efficiency of decision making in the NICE single technology appraisal process. Pharmacoecon Open. 2019;3(3):403–10.
Google Scholar | Crossref | Medline44. Eckermann, S, Karnon, J, Willan, AR. The value of value of information: best informing research design and prioritization using current methods. Pharmacoeconomics. 2010;28(9):699–709.
Google Scholar | Crossref | Medline | ISI45. Willan, AR, Eckermann, S. Value of information and pricing new healthcare interventions. Pharmacoeconomics. 2012;30(6):447–59.
Google Scholar | Crossref46. Eckermann, S, Willan, AR. Globally optimal trial design for local decision making. Health Econ. 2009;18(2):203–16.
Google Scholar | Crossref | Medline47. Eckermann, S, Willan, AR. Optimal global value of information trials: better aligning manufacturer and decision maker interests and enabling feasible risk sharing. Pharmacoeconomics. 2013;31(5):393–401.
Google Scholar | Crossref | Medline | ISI48. Willan, AR, Eckermann, S. Optimal clinical trial design using value of information methods with imperfect implementation. Health Econ. 2010;19(5):549–61.
Google Scholar | Medline49. Grimm, SE, Dixon, S, Stevens, JW. Assessing the expected value of research studies in reducing uncertainty and improving implementation dynamics. Med Decis Making. 2017;37(5):523–33.
Google Scholar | SAGE Journals50. 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 | Crossref51. Griffin, SC, Claxton, KP, Palmer, SJ, Sculpher, MJ. Dangerous omissions: the consequences of ignoring decision uncertainty. Health Econ. 2011;20(2):212–24.
Google Scholar | Crossref | Medline52. Grimm, SE, Strong, M, Brennan, A, Wailoo, AJ. The HTA risk analysis chart: visualising the need for and potential value of managed entry agreements in health technology assessment. Pharmacoeconomics. 2017;35(12):1287–96.
Google Scholar | Crossref

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