Review of guidelines for good practice in decision-analytic modelling in health technology assessment


Decision-analytic models represent an explicit way to synthesise evidence currently available on the outcomes and costs of alternative (mutually exclusive) health care interventions. This review aimed to identify and describe published guidelines for assessing the quality of decision-analytic models in health technology assessment; develop a synthesised guideline and accompanying checklist using available good practice guidelines; and provide guidance on key theoretical, methodological and practical issues not yet covered in published guidelines. The implications of this research for what might be expected of future decision-analytic models relating to health technology assessments, was then considered.


The review of current guidelines showed that although authors may provide a consistent message regarding some aspects of modelling, in other areas conflicting attributes are presented in different guidelines.

A preliminary assessment showed that, in general, the checklist developed appears to perform well, in terms of identifying those aspects of the model that should be of particular concern to the reader. The checklist cannot, however, provide answers to the appropriateness of the model structure and structural assumptions, as these may be seen as a general problem with generic checklists and do not reflect any shortcoming with the synthesised guidance and checklist developed here. The assessment of the checklist indicated the importance of its use in conjunction with a more general checklist or guidelines on economic evaluation.

The review of current guidance for good quality decision-analytic modelling for health technology assessment highlighted a number of methodological areas that have not received attention in the literature on good practice. There are a lot of these areas and, therefore, it was only possible to consider two specific methods areas in decision modelling: the identification of parameter estimates from published literature, and the issue of adjusting treatment effect estimates taken from observational studies for potential bias. Literature reviews showed that both of these areas are under-researched and are areas in which further research is needed.

Conducted by: Z Philips1, L Ginnelly1, M Sculpher1, K Claxton1,2, S Golder3, R Riemsma3, N Woolacott3, J Glanville3

1. Centre for Health Economics; 2. Department of Economics, University of York; 3. Centre for Reviews and Dissemination

Further details

Project page on HTA Programme website


Good practice guidelines for decision-analytic modelling in health technology assessment: a review and consolidation of quality of assessment. Pharmacoeconomics. 2006;24(4):355-71

Philips Z, Ginnelly L, Sculpher M, Claxton K, Golder S, Riemsma R, Woolacott N, Glanville J. Review of guidelines for good practice in decision analytic modelling in health technology assessment. Health Technol Assess. 2004;8(36):1-172


Golder S, Ginnelly L, Glanville J. Database searching for information to populate decision-analytic models: a case study. Health Technology Assessment International (HTAi) Annual Meeting; 2004 May/June; Krakow, Poland


Commissioned by the HTA Programme