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PharmacoEconomics
Published in: PharmacoEconomics 4/2006

01-04-2006 | Review Article

Good Practice Guidelines for Decision-Analytic Modelling in Health Technology Assessment

A Review and Consolidation of Quality Assessment

Authors: Dr Zoë Philips, Laura Bojke, Mark Sculpher, Karl Claxton, Su Golder

Published in: PharmacoEconomics | Issue 4/2006

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Abstract

The use of decision-analytic modelling for the purpose of health technology assessment (HTA) has increased dramatically in recent years. Several guidelines for best practice have emerged in the literature; however, there is no agreed standard for what constitutes a ‘good model’ or how models should be formally assessed. The objective of this paper is to identify, review and consolidate existing guidelines on the use of decision-analytic modelling for the purpose of HTA and to develop a consistent framework against which the quality of models may be assessed.
The review and resultant framework are summarised under the three key themes of Structure, Data and Consistency. ‘Structural’ aspects relate to the scope and mathematical structure of the model including the strategies under evaluation. Issues covered under the general heading of ‘Data’ include data identification methods and how uncertainty should be addressed. ‘Consistency’ relates to the overall quality of the model.
The review of existing guidelines showed that although authors may provide a consistent message regarding some aspects of modelling, such as the need for transparency, they are contradictory in other areas. Particular areas of disagreement are how data should be incorporated into models and how uncertainty should be assessed.
For the purpose of evaluation, the resultant framework is applied to a decision-analytic model developed as part of an appraisal for the National Institute for Health and Clinical Excellence (NICE) in the UK. As a further assessment, the review based on the framework is compared with an assessment provided by an independent experienced modeller not using the framework.
It is hoped that the framework developed here may form part of the appraisals process for assessment bodies such as NICE and decision models submitted to peer review journals. However, given the speed with which decision-modelling methodology advances, there is a need for its continual update.
Footnotes
1
Mutually exclusive healthcare strategies may include sequences or combinations of therapies. Each distinct sequence or combination should be modelled separately.
 
2
Decision-tree models are often symmetrical. This means that all of the strategies under evaluation model prognosis in the same way and that the financial costs, mortality and morbidity that can accrue from one action in one context also accrue from that same action in another context.[12]
 
Literature
1.
Briggs AH, Goeree R, Blackhouse G, et al. Probabilistic analysis of cost-effectiveness models: choosing between treatment strategies for gastroesophagael reflux disease. Med Decis Making 2002; 22: 290–308PubMed
2.
Claxton K. The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. J Health Econ 1999; 18 (3): 341–364PubMedCrossRef
3.
Claxton K, Sculpher M, Drummond M. A rational framework for decision making by the National Institute for Clinical Excellence. Lancet 2002; 360: 711–715PubMedCrossRef
4.
National Institute for Clinical Excellence. Guide to the methods of technology appraisal. London: NICE, 2004
5.
Philips Z, Ginnelly L, Sculpher M, et al. Review of guidelines for good practice in decision-analytic modelling in health technology assessment. Health Technol Assess 2004; 8 (36): iii–iv, ix–xi, 1–158PubMed
6.
Sculpher M, Fenwick E, Claxton K. Assessing quality in decision analytic cost-effectiveness models: a suggested framework and example of application. Pharmacoeconomics 2000; 17 (5): 461–477PubMedCrossRef
7.
Kendrick M, Johnson K. Long term treatment of Multiple Sclerosis with interferon beta may be cost effective. Pharmacoeconomics 2000; 18: 45–53PubMedCrossRef
8.
Decision analytic modelling in the economic evaluation of health technologies: a consensus statement. Phannacoeconomics 2000; 17 (5): 443–4
9.
Buxton MJ, Drummond MF, Van Hout BA, et al. Modelling in economic evaluation: an unavoidable fact of life. Health Econ 1997; 6 (3): 217–227PubMedCrossRef
10.
Eddy DM. Technology assessment: the role of mathematical modelling. In: Committee for Evaluating Medical Technologies in Clinical Use, Institute of Medicine, editors. Assessing medical technologies. Washington, DC: National Academy Press, 1985: 144–160
11.
Habbema JD, Bossuyt PM, Dippel DW, et al. Analysing clinical decision analyses. Stat Med 1990; 9 (11): 1229–1242PubMedCrossRef
12.
Halpern MT, Luce BR, Brown RE, et al. Health and economic outcomes modeling practices: a suggested framework. Value Health 1998; 1 (2): 131–147PubMedCrossRef
13.
Hay J, Jackson J. Panel 2: methodological issues in conducting pharmacoeconomic evaluations: modeling studies. Value Health 1999; 2 (2): 78–81PubMedCrossRef
14.
ISPOR Task Force. Principles of good practice for decisionanalytic modeling in health care evaluation. Value Health 2003; 6: 9–17CrossRef
15.
McCabe C, Dixon S. Testing the validity of cost-effectiveness models. Pharmacoeconomics 2000; 17 (5): 501–513PubMedCrossRef
16.
Nuijten MJ, Pronk MH, Brorens MJ, et al. Reporting format for economic evaluation: part II. Focus on modelling studies. Pharmacoeconomics 1998; 14 (3): 259–268PubMedCrossRef
17.
Ramsey SD. Evaluating evidence from a decision analysis. J Am Board Fam Pract 1999; 12 (5): 395–402PubMed
18.
Sendi PP, Craig BA, Pfluger D, et al. Systematic validation of disease models for pharmacoeconomic evaluations: Swiss HIV Cohort Study. J Eval Clin Pract 1999; 5 (3): 283–295PubMedCrossRef
19.
Sonnenberg FA, Roberts MS, Tsevat J, et al. Toward a peer review process for medical decision analysis models. Med Care 1994; 32 (7 Suppl.): JS52–JS64PubMed
20.
Soto J. Health economic evaluations using decision analytic modeling: principles and practices. Utilization of a checklist to their development and appraisal. Int J Technol Assess Health Care 2002; 18 (1): 94–111PubMed
21.
Weinstein MC, Toy EL, Sandberg EA, et al. Modeling for health care and other policy decisions: uses, roles, and validity. Value Health 2001; 4 (5): 348–361PubMedCrossRef
22.
Briggs AH. Handling uncertainty in cost-effectiveness models. Pharmacoeconomics 2000; 17 (5): 479–500PubMedCrossRef
Metadata
Title
Good Practice Guidelines for Decision-Analytic Modelling in Health Technology Assessment
A Review and Consolidation of Quality Assessment
Authors
Dr Zoë Philips
Laura Bojke
Mark Sculpher
Karl Claxton
Su Golder
Publication date
01-04-2006
Publisher
Springer International Publishing
Published in
PharmacoEconomics / Issue 4/2006
Print ISSN: 1170-7690
Electronic ISSN: 1179-2027
DOI
https://doi.org/10.2165/00019053-200624040-00006

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