Scenario drafting to anticipate future developments in technology assessment

BMC Research Notes

Volumn 5, Issue , 2012, Pages

  Retèl, Valesca P ^a   Joore, Manuela A ^b,c   Linn, Sabine C ^a   Rutgers, Emiel Jt ^a   Van Harten, Wim H ^a,d  

^a ANTONI VAN LEEUWENHOEK HOSPITAL   (Netherlands)

^b MAASTRICHT UNIVERSITY   (Netherlands)

^c MAASTRICHT UNIVERSITY MEDICAL CENTER   (Netherlands)

^d UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

70 gene signature; Breast cancer; Early technology assessment; Genomic profiling; Scenario drafting

Indexed keywords

ANTINEOPLASTIC AGENT;

ARTICLE; BIOMEDICAL TECHNOLOGY ASSESSMENT; BREAST TUMOR; COMPUTER SIMULATION; COST BENEFIT ANALYSIS; DECISION SUPPORT SYSTEM; DNA MICROARRAY; ECONOMICS; FEMALE; FORECASTING; GENE EXPRESSION PROFILING; GENETIC PREDISPOSITION; GENETIC SCREENING; GENETICS; HEALTH CARE COST; HUMAN; MASS COMMUNICATION; MIDDLE AGED; PATIENT SELECTION; PRACTICE GUIDELINE; PREDICTIVE VALUE; PROBABILITY; QUALITY ADJUSTED LIFE YEAR; QUESTIONNAIRE; STATISTICAL MODEL; TIME; UNCERTAINTY;

ANTINEOPLASTIC AGENTS; BREAST NEOPLASMS; COMPUTER SIMULATION; COST-BENEFIT ANALYSIS; DECISION SUPPORT TECHNIQUES; DIFFUSION OF INNOVATION; FEMALE; FORECASTING; GENE EXPRESSION PROFILING; GENETIC PREDISPOSITION TO DISEASE; GENETIC TESTING; HEALTH CARE COSTS; HUMANS; MARKOV CHAINS; MIDDLE AGED; MODELS, ECONOMIC; MODELS, STATISTICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATIENT SELECTION; PRACTICE GUIDELINES AS TOPIC; PREDICTIVE VALUE OF TESTS; QUALITY-ADJUSTED LIFE YEARS; QUESTIONNAIRES; TECHNOLOGY ASSESSMENT, BIOMEDICAL; TIME FACTORS; UNCERTAINTY;

EID: 84864957291     PISSN: None     EISSN: 17560500     Source Type: Journal    
DOI: 10.1186/1756-0500-5-442     Document Type: Article

References (40)

1. A Rational Framework for Decision Making by the National Institute for Clinical Excellence (NICE). Claxton KP, Sculpher MJ, Drummond MF, The Lancet 2002 360 9334 711 715 10.1016/S0140-6736(02)09832-X
2. Drummond MF, Sculpher MJ, Torrance GW, O'Brien BJ, Stoddart GL, Methods for the Economic Evaluation of Health Care Programmes Oxford University Press, Oxford third edition 2006
3. Improving the Efficiency and Relevance of Health Technology Assessment: the Role of Iterative Decision Analytic Modelling. Fenwick E, Claxton KP, Sculpher MJ, Briggs A, Technical Report 179 Centre for Health Economics, University of York, UK 2000
4. Spiegelhalter DJ, Abrams KR, Myles JP, Bayesian Approaches to Clinical Trials and Health-Care Evaluation Statistics in Practice. John Wiley & Sons Ltd, Chichester 2004
5. Dangerous Omissions: The Consequences of Ignoring Decision Uncertainty. Griffin SC, Claxton KP, Palmer SJ, Sculpher MJ, Health Econ 2011 20 2 212 24 10.1002/hec.1586 20091763
6. Is molecular profiling ready for use in clinical decision making? Ioannidis JP, The Oncologist 2007 12 301 311 10.1634/theoncologist.12-3-301 17405894
7. Problems in the economic appraisal of new health technology: The evaluation of heart transplants in the UK. Buxton MJ, Economic appraisal of health technology in the European Community Oxford Medical Publications, Oxford, Drummond MF, 1987 103 118
8. Gene expression profiling predicts clinical outcome of breast cancer. Van 't Veer LJ, Dai H, Van de Vijver MJ, et al. Nature 2002 415 530 536 10.1038/415530a 11823860
9. A Gene-Expression Signature as a Predictor of Survival in Breast Cancer. Van de Vijver MJ, He YD, van 't Veer LJ, et al. N Engl J Med 2002 347 1999 2009 10.1056/NEJMoa021967 12490681
10. Validation and Clinical Utility of a 70-Gene Prognostic Signature for Women With Node-Negative Breast Cancer. Buyse M, Loi S, van't Veer L, et al. J Natl Cancer Inst 2006 98 1183 1192 10.1093/jnci/djj329 16954471
11. Validation of 70-gene prognosis signature in node-negative breast cancer. Bueno-de-Mesquita JM, Linn SC, Keijzer R, et al. Breast Cancer Res Treat 2009 117 483 495 10.1007/s10549-008-0191-2 18819002
12. Use of 70-gene signature to predict prognosis of patients with node-negative breast cancer: a prospective community-based feasibility study (RASTER). Bueno-de-Mesquita JM, van Harten W, Retèl V, et al. Lancet Oncol 2007 8 1079 1087 10.1016/S1470-2045(07)70346-7 18042430
13. Clinical application of the 70-gene profile: the MINDACT trial. Cardoso F, Van't Veer L, Rutgers E, et al. J Clin Oncol 2008 26 729 735 10.1200/JCO.2007.14.3222 18258980
14. How comparative effectiveness research can help advance 'personalized medicine' in cancer treatment. Ramsey SD, Veenstra D, Tunis SR, et al. Health Aff (Millwood) 2011 30 12 2259 68 10.1377/hlthaff.2010.0637
15. Methodology of constructive technology assessment in health care. Douma KF, Karsenberg K, Hummel MJ, et al. Int J Technol Assess Health Care 2007 23 162 168 17493301
16. Constructive Technology assessment and Technology Dynamics: The Case of Clean Technologies. Schot JW, Science, Technology & Human Values 1992 17 36 56 10.1177/016224399201700103 22970425
17. The Past and Future of Constructive Technology Assessment. Schot J, Rip A, Technological Forecasting and Social Change 1996 54 251 268
18. Tumour tissue: who is in control? Ploem MC, Retèl VP, Linn SC, et al. Lancet Oncol 2010 11 9 11 10.1016/S1470-2045(09)70318-3 19880349
19. Constructive Technology Assessment (CTA) as a tool in coverage with evidence development: the case of the 70-gene prognosis signature for breast cancer diagnostics. Retèl VP, Bueno-de-Mesquita JM, Hummel MJ, et al. Int J Technol Assess Health Care 2009 25 73 83 10.1017/S0266462309090102 19126254
20. Cost-effectiveness of the 70-gene signature versus St. Gallen guidelines and Adjuvant Online for early breast cancer. Retèl VP, Joore MA, Knauer M, et al. Eur J Cancer 2010 46 1382 1391 10.1016/j.ejca.2010.02.035 20359886
21. Progress and promise: highlights of the international expert consensus on the primary therapy of early breast cancer 2007. Goldhirsch A, Wood WC, Gelber RD, et al. Ann Oncol 2007 18 1133 1144 10.1093/annonc/mdm271 17675394
22. Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. Ravdin PM, Siminoff LA, Davis GJ, et al. J Clin Oncol 2001 19 980 991 11181660
23. Expert panel vs decision-analysis recommendations for post discharge coronary angiography after myocardial infarction. Kuntz KM, Tsevat J, Weinstein MC, et al. JAMA 1999 282 2246 2251 10.1001/jama.282.23.2246 10605977
24. Implementation by simulation; strategies for ultrasound screening for hip dysplasia in the Netherlands. Ramwadhdoebe S, Van Merode GG, Boere-Boonekamp MM, et al. BMC Health Serv Res 2010 10 75 10.1186/1472-6963-10-75 20331865
25. The Economic Value of Innovative Treatments over the Product Life Cycle: the case of Targeted Trastuzumab Therapy for Breast Cancer. Garrison LP, Veenstra DL, Value Health 2009 12 1118 1123 10.1111/j.1524-4733.2009.00572.x 19624617
26. Rogers EM, Diffusion of Innovations Free Press, New York 2003
27. The EORTC 10041/BIG 03-04 MINDACT trial is feasible: results of the pilot phase. Rutgers E, Piccart-Gebhart MJ, Bogaerts J, et al. Eur J Cancer 2011 47 18 2742 9 10.1016/j.ejca.2011.09.016 22051734
28. Royal Dutch Shell Company, 2010 7 6 20 Available at: http://www.shell. com/home/content/aboutshell/our-strategy/shell-global-scenarios/ dir-global-scenarios-07112006.html
29. Scenarios: uncharted waters ahead. Wack P, Harvard Business Review 1985 63 5 73 89
30. Scenarios: shooting the rapids. Wack P, Harvard Business Review 1985 63 6 139 150
31. Oostenbrink JB, Koopmanschap MA, Rutten FFH, Manual for cost analyses, methods and standard prices for economic evaluations in health care Dutch Health Insurance Executive Board, Amstelveen (The Netherlands) 2006 in Dutch
32. Economic Evaluation and Decision Making in the UK. Buxton MJ, Pharmacoeconomics 2006 24 11 1133 42 10.2165/00019053-200624110-00009 17067197
33. Definition, interpretation and calculation of cost-effectiveness acceptability curves. Lothgren M, Zethraeus N, Health Econ 2000 9 623 630 10.1002/1099-1050(200010)9:7<623::AID-HEC539>3.0.CO;2-V 11103928
34. Representing uncertainty: the role of cost-effectiveness acceptability curves. Fenwick E, Claxton K, Sculpher M, Health Econ 2001 10 779 787 10.1002/hec.635 11747057
35. Bridging trial and decision: a checklist to frame health technology assessments for resource allocation decisions. Grutters JP, Seferina SC, Tjan-Heijnen VC, et al. Value Health 2011 14 5 777 84 10.1016/j.jval.2011.01.005 21839418
36. Integrating health economics modeling in the product development cycle of medical devices: a Bayesian approach. Vallejo-Torres L, Steuten LM, Buxton MJ, et al. Int J Technol Assess Health Care 2008 24 459 464 18828941
37. Retèl VP, Grutters JPC, van Harten WH, Joore MA, Value of research and value of development in early stages of development of new medical technologies Dissertation, available at: http://doc.utwente.nl/78236/
38. Threshold for therapies: highlights of the St Gallen international expert consensus on the primary therapy of early breast cancer 2009. Goldhirsch A, Ingle JN, Gelber RD, et al. Ann Oncol 2009 20 1319 1329 10.1093/annonc/mdp322 19535820
39. Eliciting distributions to populate decision analytic models. Bojke L, Claxton K, Bravo-Vergel Y, et al. Value Health 2010 13 557 564 10.1111/j.1524-4733.2010.00709.x 20345548
40. Locally advanced breast cancers are more likely to present as Interval Cancers: results from the I-SPY 1 TRIAL (CALGB 150007/150012, ACRIN 6657, InterSPORE Trial). Lin C, Buxton MB, Moore D, et al. Breast Cancer Res Treat 2012 132 3 871 879 10.1007/s10549-011-1670-4 21796368