Probabilistic sensitivity analysis for NICE technology assessment: Not an optional extra

Health Economics

Volumn 14, Issue 4, 2005, Pages 339-347

  Claxton, Karl ^a   Sculpher, Mark ^a   McCabe, Chris ^b   Briggs, Andrew ^c   Akehurst, Ron ^b   Buxton, Martin ^d   Brazier, John ^b   O'Hagan, Tony ^b  

^a UNIVERSITY OF YORK   (United Kingdom)

^b UNIVERSITY OF SHEFFIELD   (United Kingdom)

^c UNIVERSITY OF OXFORD   (United Kingdom)

^d BRUNEL UNIVERSITY   (United Kingdom)

Author keywords

Cost effectiveness analysis; Health technology assessment; National institute for clinical excellence; Probabilistic sensitivity analysis

Indexed keywords

BIOMEDICAL TECHNOLOGY ASSESSMENT; CONSULTATION; COST EFFECTIVENESS ANALYSIS; FINANCIAL MANAGEMENT; HEALTH CARE ORGANIZATION; HEALTH CARE POLICY; HEALTH CARE SYSTEM; MEDICAL DECISION MAKING; METHODOLOGY; PRACTICE GUIDELINE; PRIORITY JOURNAL; PROBABILITY; REVIEW; SENSITIVITY ANALYSIS;

COST-BENEFIT ANALYSIS; DECISION SUPPORT TECHNIQUES; GUIDELINES; MODELS, STATISTICAL; TECHNOLOGY ASSESSMENT, BIOMEDICAL;

EID: 17144409710     PISSN: 10579230     EISSN: None     Source Type: Journal    
DOI: 10.1002/hec.985     Document Type: Review

References (29)

1. Hjelmgren J, Berggren F, Andersson F. Health economic guidelines - similarities, differences and some implications. Value Health 2001; 4(3): 225-250.
2. Claxton K, Sculpher M, Drummond M. A rational framework for decision making by the National Institute for Clinical Excellence. Lancet 2002; 360: 711-715.
3. National Institute for Clinical Excellence (NICE). Guide to the Methods of Technology Appraisal. NICE: London, 2004.
4. Gold MR, Siegel JE, Russell LB, Weinstein MC. Cost-Effectiveness in Health and Medicine. Oxford University Press: New York, 1996.
5. Langley PC. The NICE reference case requirement: implications for drug manufacturers and health systems. Pharamacoeconomics 2004; 22: 267-271.
6. Gafni A, Birch S. The NICE reference case requirement: more pain for what, if any, gain? Pharamacoeconomics 2004; 22: 271-273.
7. Critchfield GC, Willard KE, Connelly DP. Probabilistic analysis of decision trees using Monte Carlo simulation. Med Decision Making 1986; 6: 85-92.
8. Doubilet P, Begg CB, Weinstein MC, Braun P, McNeil BJ. Probabilistic sensitivity analysis using Monte Carlo simulation. Med Decision Making 1985; 5: 157-177.
9. Briggs AH, Goeree R, Blackhouse G, O'Brien BJ. Probabilistic analysis of cost-effectiveness models: choosing between treatment strategies for gastroesophageal reflux disease. Med Decision Making 2002; 22: 290-308.
10. Van Hout BA, Al MJ, Gordon GS, Rutten FFH. Costs, effects and c/e-ratios alongside a clinical trial. Health Econ 1994; 3: 309-319.
11. Briggs AH. A Bayesian approach to stochastic cost-effectiveness analysis. Health Econ 1999; 8: 257-262.
12. Fenwick E, Claxton K, Sculpher M. Representing uncertainty: the role of cost-effectiveness acceptability curves. Health Econ 2001; 10: 779-789.
13. Chilcott J, McCabe C, Tappenden P, O'Hagan A, Cooper NJ, Abrams K et al. Modelling the cost effectiveness of interferon beta and glatiramer acete in the management of multiple sclerosis. Br Med J 2003; 326: 522.
14. Claxton K. The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. J Health Econ 1999; 18: 342-364.
15. National Institute for Clinical Excellence. Guidance on the Selection of Prostheses for Primary Total Hip Replacement (www.nice.org.uk). National Institute for Clinical Excellence: London, 2000.
16. National Institute for Clinical Excellence. Health Technology Assessment Report on Liquid Based Cytology (www.nice.org.uk). National Institute for Clinical Excellence: London, 2000.
17. National Institute of Clinical Excellence (NICE). Guidance on the Use of Laparoscopic Surgery for Colorectal Cancer (www.nice.org.uk). National Institute for Clinical Excellence: London, 2000.
18. Claxton K, Posnett J. An economic approach to clinical trial design and research priority-setting. Health Econ 1996; 5: 513-524.
19. Palmer S, Smith PC. Incorporating option values into the economic evaluation of health care technologies. J Health Econ 2000; 19: 755-766.
20. Meltzer D. Addressing uncertainty in medical cost-effectiveness analysis. Implications of expected utility maximization for methods to perform sensitivity analysis and the use of cost-effectiveness analysis to set priorities for medical research. J Health Econ 2001; 20: 109-129.
21. Ades AE, Cliffe S. Markov Chain Monte Carlo estimation of multi-parameter decision model: consistency of evidence and the accurate assessment of uncertainty. Med Decision Making 2002; 22: 359-371.
22. Ades AE, Lu G, Claxton K. Expected value of sample information in medical decision modelling. Med Decision Making 2003; 24: 207-227.
23. O'Brien BJ, Drummond MF, Labelle RJ, Willan A. In search of power and significance: issues in the design and analysis of stochastic cost-effectiveness studies in health care. Med Care 1994; 32 2): 150-163.
24. Karnon J. Alternative decision modelling techniques for evaluation of health care technologies: Markov processes verses discrete event simulation. Health Econ 2003; 12: 837-848.
25. Stevenson MD, Oakley J, 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 Decision Making 2004; 24: 89-100.
26. Stevenson M, Jones LM, de Nigris E, Brewer N, Oakley J. The Clinical Effectiveness and Cost Effectiveness of Prevention and Treatment of Osteoporosis (www.nice.org.uk). National Institute for Clinical Excellence: London, 2003.
27. Briggs AH, O'Brien BJ, Blackhouse G. Thinking outside the box: recent advances in the analysis and presentation of uncertainty in cost-effectiveness studies. Ann Rev Public Health 2002; 23: 377-401.
28. Black WC. A graphic representation of cost-effectiveness. Med Decision Making 1990; 10: 212-214.
29. Claxton K, Neuman PJ, Araki SS, Weinstein MC. The value of information: an application to a policy model of Alzheimer's disease. Int J Technol Assess Health Care 2001; 17: 38-55.