Methods for expected value of information analysis in complex health economic models: Developments on the health economics of interferon-β and glatiramer acetate for multiple sclerosis

Health Technology Assessment

Volumn 8, Issue 27, 2004, Pages

  Tappenden, P ^a   Chilcott, J B ^a   Eggington, S ^a   Oakley, J ^a   McCabe, C ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BETA INTERFERON; BETA1A INTERFERON; GLATIRAMER; INTERFERON BETA SERINE;

ACCURACY; ANALYTIC METHOD; ANALYTICAL ERROR; ARTIFICIAL NEURAL NETWORK; COMPUTER PROGRAM; CORRELATION ANALYSIS; COST EFFECTIVENESS ANALYSIS; DATA ANALYSIS; DATA BASE; DISEASE COURSE; DRUG COST; EPIDEMIOLOGICAL DATA; HEALTH CARE COST; HEALTH ECONOMICS; HUMAN; INTERMETHOD COMPARISON; INTERNET; LINEAR REGRESSION ANALYSIS; MATHEMATICAL COMPUTING; MEDICAL RESEARCH; MEDICAL SPECIALIST; METHODOLOGY; MONTE CARLO METHOD; MULTIPLE SCLEROSIS; NORMAL DISTRIBUTION; PHYSIOTHERAPY; PREDICTION; PROBABILITY; PSYCHOTHERAPY; QUALITY ADJUSTED LIFE YEAR; QUALITY OF LIFE; RELAPSE; REVIEW; RISK ASSESSMENT; SENSITIVITY ANALYSIS; SIDE EFFECT; SIMULATION; SOCIAL SUPPORT; STATISTICAL MODEL; TREATMENT OUTCOME; TREATMENT WITHDRAWAL;

EID: 3042525973     PISSN: 13665278     EISSN: None     Source Type: Journal    
DOI: 10.3310/hta8270     Document Type: Review

References (76)

1. Brennan A, Chilcott JB, Kharroubi A, O'Hagan A. Calculating expected value of perfect information: resolution of conflicting methods via a two level Monte Carlo approach. URL: http://www.shef.ac.uk/chebs/FFortnights/FF2/Brennan.doc. Accessed 17 February 2003.
2. Palmer S, Smith PC. Incorporating option values into the economic evaluation of health care technologies. J Health Econ 2003;19:755-66.
3. Briggs AH, Gray AM. Handling uncertainty when performing economic evaluation of healthcare interventions. Health Technol Assess 1999;3(2).
4. Raiffa H. Decision Analysis: Introductory Lectures on Choice Under Uncertainty. Reading, MA: Addison-Wesley; 1968.
5. Thompson MS. Decision-analytic determination of study size: the case of electronic fetal monitoring. Med Decis Making 1981;1:165-79.
6. Phelps CE, Mushlin AI. Focusing technology assessment using medical decision theory. Med Decis Making 1988;8:279-89.
7. Eddy DM. Research Planning. In Eddy DM, Hasselblad V, Shachter RD, editors. Meta-analysis by the confidence profile method. The statistical synthesis of evidence. Boston, MA: Academic Press. 1992. pp. 313-22.
8. Hornberger JC, Brown BW, Halpern J. Designing a cost effective clinical trial. Stat Med 1995; 14:2249-59.
9. Hornberger J, Eghtesady P. The cost benefit of a randomised trial to a health care organisation. Control Clini Trials 1998;19:198-211.
10. Claxton K, Posnett J. An economic approach to clinical trial design and research priority-setting. Health Econ 1996;5:513-24.
11. Claxton K. The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. J Health Econ 1999;18:341-64.
12. Felli C, Hazen GB. Sensitivity analysis and the expected value of perfect information. Med Decis Making 1998;18:95-109.
13. Felli JC, Hazen GB. A Bayesian approach to sensitivity analysis. Health Econ 1999;8:263-8.
14. Chilcott JB, Brennan AB, Booth A, Karnon J, Tappenden P. The role of modelling in prioritising and planning clinical trials. Health Technol Assess 2003;7(23).
15. Claxton K, Sculpher M, Drummond M. A rational framework for decision making by the National Institute for Clinical Excellence (NICE). Lancet 2002;360:711-15.
16. Fenwick E, Claxton K, Sculpher M, Briggs A. Improving the efficiency and relevance of health technology assessment: the role of iterative decision analytic modelling. CHE Discussion Paper 179. York: University of York, Centre for Health Economics. 2000.
17. Claxton K, Neumann PJ, Araki S, Weinstein MC. Bayesian value-of-information analysis. An application to a policy model of Alzheimer's disease. Int J Technol Assess Health Care 2001;17:38-55.
18. Payne N, Chilcott JB, McGoogan E. Liquid-based cytology in cervical screening: a rapid and systematic review. Health Technol Assess 2000;4(18).
19. Wight JP, Chilcott JB, Holmes MW, Brewer N. The clinical and cost-effectiveness of pulsatile machine perfusion versus cold storage of kidneys for transplantation retrieved from heart-beating and non-heart-beating donors. Health Technol Assess 2003;7(25).
20. Pandor A, Eastham J, Beverley C, Chilcott J, Paisley S. Clinical and cost-effectiveness of neonatal screening for inborn errors of metabolism using tandem mass spectrometry: a systematic review. Health Technol Assess 2004;8(12).
21. Claxton K, Ginnelly L, Sculpher M, Palmer S, Philips ZMJ. Pilot study of using value of information analysis to set priorities in research and development. Health Technol Assess 2004 (in press).
22. Briggs AH, Goeree R, Blackhouse G, O'Brien BJ. Probabilistic analysis of cost-effectiveness models: choosing between treatment strategies for gastroesophageal reflux disease. Med Decis Making 2003;22:290-308.
23. Chilcott JB, McCabe CM, Tappenden P, O'Hagan A, Cooper NJ, Claxton K, et al. Modelling the cost effectiveness of interferon beta and glatiramer acetate in the management of multiple sclerosis. BMJ 2003;326:522.
24. Compston A, Ebers G, Lassman H, McDonald I, Matthews B, Wekerle H. McAlpine's multiple sclerosis. London: Churchill Livingstone; 1998.
25. Richards RG, Beard SM, Sampson FC, Tappenden P. A review of the natural history and epidemiology of multiple sclerosis: implications for resource allocation and health economic models. Health Technol Assess 2002;6:1-73.
26. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale. Neurology 1983;33:1444-52.
27. Kobelt G, Lindgren P, Parkin D, Francis DA, Johnson M, Bates D, et al. Costs and quality of life in multiple sclerosis. A cross-sectional observational study in the UK. Scandinavian Working Papers in Economics; 2000; URL: http://www.swopec.hhs.se/hastef/papers/hastef0398.pdf. Accessed 12 May 2001.
28. Chadwick D, Compston A, Miller D, ZajicekJ, Dick J, Martyn C, et al. Guidelines for the use of beta interferons and glatiramer acetate in multiple sclerosis. London: Association of British Neurologists; 2001. URL: http://www.theabn.org/downnloads/msdoc.pdf. Accessed 14 August 2003.
29. NHS Executive. EL 94 72. Leeds: Department of Health; 1994.
30. Brown MG, Murray TJ, Sketris IS, Fisk JD, LeBlanc JC, Schwartz CE, et al. Cost-effectiveness of interferon beta-1b in slowing multiple sclerosis disability progression. First estimates. Int J Technol Assess Health Care 2000;16:751-67.
31. Kendrick M, Johnson KI. Long-term treatment of multiple sclerosis with interferon-beta may be cost effective. [Review] [17 refs]. Pharmacoeconomics 2000;18:45-53.
32. Forbes RB, Lees A, Waugh N, Swingler RJ. Population based cost utility study of interferon beta-1b in secondary progressive multiple sclerosis [comment]. BMJ 1999;319:1529-33.
33. National Institute for Clinical Excellence. Minutes of the technology appraisals committee meeting 13th December 2000. URL: http://www.nice.org.uk/article.asp?a=16091. Accessed 6 May 2001.
34. National Institute for Clinical Excellence. Full guidance on the use of beta interferon and glatiramer for the treatment of multiple sclerosis. Technology Appraisal No.32. 2002.URL: http://www.nice.org.uk/Docrefasp?d=27634. Accessed 8 April 2003.
35. Tappenden P, Chilcott JB, O'Hagan A, McCabe C, Cooper NJ, Abrams KA, et al. Cost-effectiveness of beta interferon and glatiramer acetate in the management of multiple sclerosis. Report to the National Institute for Clinical Excellence. 2001.
36. Ebers, G. London Ontario cohort study: confidential data on file at National Institute for Clinical Excellence. London. 2001.
37. IFNB Multiple Sclerosis Study Group. Interferon beta-1b in the treatment of multiple sclerosis: final outcome of the randomized controlled trial. Am Acad Neurol 1995;45:1277-85.
38. PRISMS Study Group. Randomized double-blind placebo-controlled study of interferon beta-1a in relapsing/remitting multiple sclerosis. Lancet 1998;352:1498-504.
39. Jacobs LD, Cookfair DL, Rudick RR, Herndon RM, Richet JR, Salazir AM. Intramuscular interferon beta-1a for disease progression in relapsing multiple sclerosis. Am Neurol Assoc 1996;39:285-94.
40. Johnson KP, Brooks BR, Cohen JA, Ford CC, Goldstein J, Lisak RP. Copolymer 1 reduces relapse rate and improves disability in relapsing-remitting multiple sclerosis: results of a phase III multi centre, double-blind, placebo-controlled trial. Am Acad Neurol 1995;45:1268-76.
41. European Study Group on Interferon Beta-1b in Secondary Progressive MS. Placebo-controlled multicentre randomised trial of interferon beta-1b in treatment of secondary progressive multiple sclerosis. Lancet 1998;352:1491-7.
42. Patwardhan M, Matchar D, Samsa D, McCrory D, Li T, Williams R. Cost of multiple sclerosis by level of disability: a review of literature. Poster presented 22 June 2003 at International Society for Technology Assessment in Health Care, Canmore, Alberta, 2003.
43. Murphy N, Confavreux C, Haas J, König N, Roullet E, Sailor M, et al. Economic evaluation of multiple sclerosis in the UK, Germany and France. Pharmacoecomics 1998;13:607-22.
44. Parkin D, McNamee P, Jacoby A, Miller P, Thomas S, Bates D. A cost-utility analysis of beta interferon for multiple sclerosis. Health Technol Assess 1998;2(4).
45. Grima DT, Torrance GW, Francis G. Cost and health related quality of life consequences of multiple sclerosis. Mult Scler 2000;6:91-8.
46. Department of Health. Reference costs 2002. URL: http://www.doh.gov.uk/refcosts2002/collection/download.htm. Accessed 5 April 2003.
47. Barton RR. Simulation metamodels. In Medeiros DJ, Watson EF, Carson JS, Manivannan MS, editors. Proceedings of the 1998 Winter Simulation Conference. Washington. 1998.
48. 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; 50:1018-33.
49. Hussain MF, Barton RR, Joshi SB. Metamodelling: radial basis functions versus polynomials. Eur J Oper Res 2002;138:142-54.
50. Batmaz I, Tunali S. Small response surface designs for metamodel estimation. Eur J Oper Res 2003; 145:455-70.
51. Jin R, Chen W, Simpson TW. Comparative studies of metamodelling techniques under multiple modelling criteria. Struct Multidisc Optimis 2001; 23:1-13.
52. Kleijnen JPC, Sargent RG. A methodology for fitting and validating metamodels in simulation. Eur J Oper Res 2000;120,14-29.
53. Sasena MJ. Optimisation of computer simulations via smoothing splines and Kriging metamodels. Masters thesis, University of Michigan, 1998.
54. Meckesheimer M, Barton RR, Simpson TW, Limayem F, Yannou B. Metamodelling of combined discrete/continuous responses. Am Inst Aeronaut Astronaut J 2001;39:1950-9.
55. Montgomery DC. Design and analysis of experiments. New York: Wiley, 1991.
56. Hurrion RD. Using a neural network to enhance the decision making quality of a visual interactive Simulation model. J Oper Res Soc 1992;43:333-41.
57. Kilmer RA, Smith AE, Shuman LJ. Computing confidence intervals for stochastic simulation using neural network metamodels. Comp Ind Eng 1999;36:391-407.
58. Marseguerra M, Masini R, Zio E. Neural modelling for sensitivity analysis in reliability and availability calculations. In Proceedings of European Safety and Reliability International Conference. 16-20 September 2001. Torino, Italy. pp. 189-196.
59. StatSoft. Multivariate adaptive regression splines (MARSplines). URL: www.statsoftinc.com/textbook/stmars.html. Accessed 23 July 2003.
60. Myers RH, Montgomery DC. Response surface methodology: process and product optimization using designed experiments. 2nd edition. New York: Wiley. 2002.
61. Carson Y, Maria A. Simulation optimisation: methods and applications. In Andradottir S, Healy KJ, Withers DH, Nelson BL, editors Proceedings of the 1997 Winter Simulation Conference. Atlanta. 1997. pp. 118-26.
62. Simpson TW, Mauery TM, Korte JJ, Mistree F. Comparison of response surface and Kriging models for multidisciplinary design optimization. In Proceedings of the 7th Symposium on Multidisciplinary Analysis and Optimization, St Louis, MO. AIAA-98-4755. 1998.
63. Koch PN, Mavris D, Mistree F. Multi-level, Partitioned Response Surfaces for Modelling Complex Systems. In Proceedings of the 7th Symposium on Multidisciplinary Analysis and Optimisation, St Louis, MO. AIAA-98-4958. 1998.
64. O'Hagan A. Curve fitting and optimal design for prediction (with discussion). J R Stat Soc Ser B 1978;40:1-42.
65. O'Hagan A, Kennedy MC, Oakley JE. Uncertainty analysis and other inference tools for complex computer codes (with discussion). In Bernado JM, editor Bayesian statistics 6. Oxford: Oxford University Press; 1999. pp 503-24.
66. Oakley, J. E. Value of information for complex cost-effectiveness models. Research Report No. 533/02. Sheffield: Department of Probability and Statistics, University of Sheffield; 2002
67. Simpson TW, Peplinski J, Koch PN, Allen JK. Metamodels for computer-based engineering design: survey and recommendations. Eng Comput 2001;17:129-50.
68. Coyle D, Buxton M, O'Brien B. Measures of importance for economic analysis based on decision modelling. J Clin Epidemiol 2003; 56:989-97.
69. McKenna SA, Arnold BW. Quantitative sensitivity and importance analysis of parameters in Monte Carlo models. In Proceedings of the International High-level Radioactive Waste Management Conference. La Grange, IL: American Nuclear Society; 1998.
70. Bettonvil B, Kleijnen JPC. Searching for important factors in simulation models with many factors: sequential bifurcation. Eur J Oper Res 1996; 96:180-94.
71. Morrice D, Schruben L. Simulation factor screening using harmonic analysis. Manage Sci 1993;39:1459-76.
72. Cheng RCH. Searching for important factors: sequential bifurcation under uncertainty. In Andradottir S, Healij KJ, Withes DH, Nelson BL, editors Proceedings of the 1997 Winter Simulation Conference. Atlanta. 1997. pp. 275-80.
73. Oakley JE, O'Hagan A. Bayesian inference for the uncertainty distribution of computer model outputs. Biometrika 2002;89:769-84.
74. School of Health and Related Research. MS risk share scheme monitoring study: Appendix 1 - drug data. November Report. Sheffield: ScHARR; 2003.
75. National Institute for Clinical Excellence. Multiple sclerosis - national guidelines for NHS management of primary and secondary care (draft for 2nd round stakeholder consultation). London: NICE; 2003.
76. National Institute for Clinical Excellence. Guide to methods of technology appraisal. Consultation Document. London: NICE; 2003.