Calculating Expected Value of Sample Information Adjusting for Imperfect Implementation

1. Schlaifer, R . Probability and Statistics for Business Decisions. New York: McGraw-Hill; 1959.
Google Scholar2. Raiffa, H, Schlaifer, H. Applied Statistical Decision Theory. Boston: Harvard University Press; 1961.
Google Scholar3. Briggs, A, Sculpher, M, Claxton, K. Decision Modelling for Health Economic Evaluation. Oxford (UK): Oxford University Press; 2006.
Google Scholar4. Ades, A, Lu, G, Claxton, K. Expected value of sample information calculations in medical decision modeling. Med Decis Making. 2004;24:207–27.
Google Scholar | SAGE Journals5. Conti, S, Claxton, K. Dimensions of design space: a decision-theoretic approach to optimal research design. Med Decis Making. 2009;29(6):643–60.
Google Scholar | SAGE Journals6. Rothery, C, Strong, M, Koffijberg, H, 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 | Crossref7. Philips, Z, Claxton, K, Palmer, S. The half-life of truth: what are appropriate time horizons for research decisions? Med Decis Making. 2008;28(3):287–99.
Google Scholar | SAGE Journals8. Willan, A, Eckermann, S. Optimal clinical trial design using value of information methods with imperfect implementation. Health Econ. 2010;19(5):549–61.
Google Scholar | Medline9. Andronis, L, Barton, P. Adjusting estimates of the expected value of information for implementation: theoretical framework and practical application. Med Decis Making. 2016;36(3):296–307.
Google Scholar | SAGE Journals | ISI10. Grimm, S, Dixon, S, Stevens, J. 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 Journals11. Eckermann, S, Willan, A. Expected value of sample information with imperfect implementation: Improving practice and reducing uncertainty with appropriate counterfactual consideration. Med Decis Making. 2016;36(3):282–3.
Google Scholar | SAGE Journals12. Fenwick, E, Claxton, K, Sculpher, M. The value of implementation and the value of information: Combined and uneven development. Med Decis Making. 2008;28(1):21–32.
Google Scholar | SAGE Journals | ISI13. 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 Journals14. Strong, M, Oakley, J, 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 Journals15. Menzies, N . An efficient estimator for the expected value of sample information. Med Decis Making. 2016;36(3):308–20.
Google Scholar | SAGE Journals16. Jalal, H, Goldhaber-Fiebert, J, Kuntz, K. 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 Journals17. Jalal, H, Alarid-Escudero, F. A Gaussian approximation approach for value of information analysis. Med Decis Making. 2017;38(3):174–88.
Google Scholar | Medline18. 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 Journals19. Heath, A, Manolopoulou, I, Baio, G. Estimating the expected value of sample information across different sample sizes using moment matching and nonlinear regression. Med Decis Making. 2019;39(4):347–59.
Google Scholar | SAGE Journals20. Stinnett, A, Mullahy, J. Net health benefits a new framework for the analysis of uncertainty in cost-effectiveness analysis. Med Decis Making. 1998;18(2):S68–80.
Google Scholar | Medline21. Claxton, K . The irrelevance of inference: a decision-making approach to stochastic evaluation of health care technologies. J Health Econ. 1999;18:342–64.
Google Scholar | Crossref | Medline22. Briggs, A, Weinstein, M, Fenwick, E, Karnon, J, Sculpher, M, Paltiel, A; ISPOR-SMDM Modeling Good Research Practices Task Force , et al. Model parameter estimation and uncertainty: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-6. Value Health. 2012;15(6):835–42.
Google Scholar | Crossref23. Heath, A, Strong, M, Glynn, D, Kunst, N, Welton, N, Goldhaber-Fiebert, J. Simulating study data to support expected value of sample information calculations: a tutorial. Med Decis Making. August 13, 2021. https://doi.org/10.1177/0272989X211026292
Google Scholar | SAGE Journals24. Kunst, N, Wilson, E, Glynn, D, et al. Computing the expected value of sample information efficiently: practical guidance and recommendations for four model-based methods. Value Health. 2020;23(6):734–42.
Google Scholar | Crossref | Medline25. Heath, A, Baio, G. Calculating the expected value of sample information using efficient nested monte carlo: a tutorial. Value Health. 2018;21(11):1299–304.
Google Scholar | Crossref | Medline26. Strong, M, Oakley, J, 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 Journals27. Richards, FJ . A flexible growth function for empirical use. J Exp Botany. 1959;10(2):290–301.
Google Scholar | Crossref28. Eckermann, S . Health Economics from Theory to Practice. New York: Springer; 2017.
Google Scholar | Crossref

留言 (0)

沒有登入
gif