Artificial neural network metamodel for sensitivity analysis in a total hip replacement health economic model

References

• NICE (National Institute for Health and Care Excellence). Guide to the methods for technology appraisal. 2013. [cited 2019 May 22]. Available from: https://www.nice.org.uk/process/pmg9/resources/guide-to-the-methods-of-technology-appraisal-2013-pdf-2007975843781
   Google Scholar
• Neuman PJ, Kim DD, Trikalinos TA, et al. Future directions for cost-effectiveness in health and medicine. Med Decis Mak. 2018;38(7):767–777.
   PubMed Web of Science ®Google Scholar
• Council NR. Assessing the reliability of complex models: mathematical and statistical foundations of verification, validation, and uncertainty quantification. Washington, DC: National Academies Press; 2012.
   Google Scholar
• Claxton K. Exploring uncertainty in cost-effectiveness analysis. Pharmacoeconomics. 2008;26(9):781–798.
   PubMed Web of Science ®Google Scholar
• Hunink MGM, Weinstein MC. Decision making in health and medicine: integrating evidence and value. Cambridge, UK: Cambridge University Press; 2001.
   Google Scholar
• Doubilet P, Begg CB, Weinstein MC, et al. Probabilistic sensitivity analysis using Monte Carlo Simulation. Med Decis Mak. 1985;5:157–177.
   PubMedGoogle Scholar
• Jalal H, Dowd B, Sainfort F, et al. Linear regression metamodeling as a tool to summarize and present simulation model results. Med Decis Mak. 2013;33(7):880–890.
   PubMed Web of Science ®Google Scholar
• Kleijnen JPC. Statistical tools for simulation practitioners. New York: M Deker; 1987.
   Google Scholar
• Sacks J, Welch WJ, Mitchell TJ, et al. Design and analysis of computer experiments. Stat Sci. 1989;4:409–435.
   Google Scholar
• Jin R, Chen W, Simpson TW. Comparative studies of techniques under multiple modelling criteria. Struct Multidiscip O. 2001;23:1–13.
   Web of Science ®Google Scholar
• Degeling K, Koffijberg H, Ijzerman MJ. A systematic review and checklist presenting the main challenges for health economic modelling in personalized medicine: towards implementing patient-level models. Expert Rev Pharmacoecon Outcomes Res. 2019;17(1):17–25.
   Web of Science ®Google Scholar
• Rojnik K, Naversnik K. Gaussian process metamodelling in Bayesian value of information analysis: a case of the complex health economic model for breast cancer screening. Value Health. 2008;11:240–250.
   PubMed Web of Science ®Google Scholar
• Jalal H, Alarid-Escudero F. A Gaussian approximation approach for value of information analysis. Med Decis Mak. 2018;38(2):174–188.
   PubMed Web of Science ®Google Scholar
• Oakley J. Value of information for complex cost-effectiveness models. Sheffield: Department of Probability and Statistics, University of Sheffield; 2003. (Research Report No. 533/02).
   Google Scholar
• Tappenden P, Chilcott JB, Egginton S, et al. Methods for expected value of information analysis in complex health economic models: developments on the health economics of interferon-beta and glatiramer acetate for multiple sclerosis. Health Technol Assess. 2004;27:1–91.
   Google Scholar
• Jalal H, Goldhaber-Fiebert JD, Kuntz KM. Computing expected value of partial sample information from probabilistic sensitivity analysis using linear regression metamodeling. Med Decis Mak. 2015;35(5):584–595.
   PubMed Web of Science ®Google Scholar
• Stevenson MD, Oakley JE, Chilcott JB. Gaussian process modelling in conjunction with individual patient simulation modelling: a case study describing the calculation of cost-effectiveness ratios for the treatment of established osteoporosis. Med Decis Mak. 2004;24:89–100.
   PubMed Web of Science ®Google Scholar
• Woodroffe R, Yao GL, Meads C, et al. Clinical and cost-effectiveness of newer immunosuppressive regimens in renal transplantation: a systematic review and modelling study. Health Technol Assess. 2005;9(21):1–179, iii-iv.
   PubMed Web of Science ®Google Scholar
• Andrianakis I, Vernon IR, McCreesh N, et al. Bayesian history matching of complex infectious disease models using emulation: a tutorial and a case study of HIV in Uganda. PloS Comput Biol. 2015;11(1):e1003968.
   PubMed Web of Science ®Google Scholar
• Alam MF, McNaught KR, Ringrose TJ. A comparison of experimental designs in the development of a neural network simulation metamodel. Simul Modell Pract Theor. 2004;12:559–578.
   Web of Science ®Google Scholar
• Briggs A, Sculpher M, Dawson J, et al. The use of probabilistic models in technology assessment: the case of total hip replacement. Appl Health Econ Health Policy. 2004;3:79–89.
   PubMedGoogle Scholar
• Briggs AH, Goeree R, Blackhouse G, et al. Probabilistic analysis of cost-effectiveness models: choosing between treatment strategies for gastroesophageal reflux disease. Med Decis Mak. 2003;22:290–308.
   Web of Science ®Google Scholar
• Briggs A, Sculpher M, Dawson J, et al. Modelling the cost-effectiveness of primary in technology assessment: how cost-effective is the Spectron compared to the Charnley prosthesis? Centre for Health Economics Technical paper no. 28, York: Centre for Health Economics, University of York. 2003.
   Google Scholar
• Morris MD. Two stage factor screening procedures using multiple grouping assignments. Commun Stat Theor M. 1987;16:3051–3067.
   Web of Science ®Google Scholar
• Campolongo F, Cariboni J, Saltelli A. An effective screening design for sensitivity analysis of large models. ‎Environ Model Softw. 2007;22:1509–1518.
   Web of Science ®Google Scholar
• Morris MD. Factorial sampling plans for preliminary computational experiments. Technometrics. 1991;33:161–174.
   Web of Science ®Google Scholar
• Alam MF, McNaught KR, Ringrose TJ. An artificial neural network based metamodel for analysing a stochastic combat simulation. Int J Enterp Inf Syst. 2006;2:38–57.
   Google Scholar
• Zhang Z, Beck AW, Winkler DA, et al. Opening the black box of neural networks: methods for interpreting neural network models in clinical applications. Ann Transl Med. 2018;6(11):216.
   PubMed Web of Science ®Google Scholar
• Hurrion RD. Using a neural network to enhance the decision making quality of a visual interactive simulation using neural network metamodels. J Oper Res Soc. 1992;43:333–341.
   Web of Science ®Google Scholar
• Rumelhurt DE, Hinton GE, Williams RJ. Learning internal representation byerror propagation. In: Rumelhurt DE, McClelland JL, editors. Paralleldistributed processing: exploration in the microstructure of cognition. Vol. 1. Cambridge, Massachusetts: MIT Press; 1986. p. 318–362.
   Google Scholar
• Haykin S. Neural networks: a comprehensive foundation. New York: Macmillan Publishing Company; 1994.
   Google Scholar
• Smith M. Neural network for statistical modelling. New York: Van Nostrand Reinhold; 1993.
   Google Scholar
• Pierreval H, Huntsinger RC. An investigation on neural network capabilities as simulation metamodels. Proceedings of the 1992 Summer Computer Simulation Conference; San Diego, California: Society for Computer Simulation; 1992. p. 413–417.
   Google Scholar
• Badiru AB, Seiger DB. Neural network as a simulation metamodel in economic analysis of risky projects. Eur J Oper Res. 1998;105:130–142.
   Web of Science ®Google Scholar
• Hurrion RD, Birgil S. A comparison of factorial and random experimental design methods for the development of regression and neural network simulation metamodels. J Oper Res Soc. 1999;50:1018–1033.
   Web of Science ®Google Scholar
• Kleijnen JPC, Sargent RG. A methodology for fitting and validating metamodels in simulation. Eur J Oper Res. 2000;120:14–29.
   Web of Science ®Google Scholar
• Lunani M, Sudjianto A, Johnston PL. Generating efficient training samples for neural networks using Latin hypercube sampling. In: Dagli CH, Akay M, Chen CLP, Fernandez BR, Ghosh J, editors. Intelligent Engineering Systems Through Artificial Neural Networks. Vol. 5. New York: ASME Press; 1995. p. 209–214.
   Google Scholar
• MATLAB Version 7.6 (R2008a). USA: The MathWorks Inc.; 2008.
   Google Scholar
• The R Foundation for Statistical Computing, R version 2.8.1 (2008-12-22).
   Google Scholar
• Centre for Bayesian statistics in health economics, University of Sheffield. [cited 2019 Mar 18]. Available from: http://www.shef.ac.uk/chebs/software
   Google Scholar
• IBM SPSS Neural Networks version 25. 2017.
   Google Scholar
• Kim S, Kim H. A new metric of absolute percentage error for intermittent demand forecasts. ‎Int J Forecast. 2016;32(3):669–679.
   Web of Science ®Google Scholar
• Swingler K. Applying neural networks: a practical guide. London: Academic Press; 1996.
   Google Scholar
• Faraway J, Chatfield C. Time series forecasting with neural networks: a comparative study using airline data. Appl Stat. 1998;47:231–250.
   Web of Science ®Google Scholar
• Richter GM, Acutis M, Trevisiol P, et al. Sensitivity analysis for a complex crop model applied to durum wheat in the Mediterranean. Eur J Agron. 2010;32(2):127–136.
   Web of Science ®Google Scholar
• Kilmer RA, Smith AE, Shuman LJ. An emergency department simulation and a neural network metamodel. J Soc Health Syst. 1997;5(3):63–79.
   PubMedGoogle Scholar