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Zoë Philips and Laura Bojke With M Sculpher, K Claxton, S Golder, R Riemsma,

Review of guidelines for good practice in decision-analytic modelling in health technology assessment. Zoë Philips and Laura Bojke With M Sculpher, K Claxton, S Golder, R Riemsma, N Woolacoot, J Glanville. MRC HSRC workshop, Leicester 25 July 2005. Background.

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Zoë Philips and Laura Bojke With M Sculpher, K Claxton, S Golder, R Riemsma,

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  1. Review of guidelines for good practice in decision-analytic modelling in health technology assessment Zoë Philips and Laura Bojke With M Sculpher, K Claxton, S Golder, R Riemsma, N Woolacoot, J Glanville MRC HSRC workshop, Leicester 25 July 2005

  2. Background • Use of decision-analytic modelling increasing • Guidelines for the appropriate use, development and appraisal of decision-analytic models appearing since 1985 • No agreed standard • Decision-analytic modelling forms an essential part of the NICE appraisals process

  3. Background • “…it will be necessary … to construct an analytical framework within which to synthesise the available evidence in order to estimate clinical and cost effectiveness relevant to the clinical decision making context. The framework will usually require the development of a model. This may be a decision-analytic model using aggregated data or a statistical model using patient level data.” Page 20 Section 5.2.2.2 NICE, Guide to the methods of technology appraisal. 2004, National Institute for Clinical Excellence: London.

  4. Objectives • Systematic review of published guidelines for assessing the quality of decision-analytic models in HTA • Development of a synthesised guideline and accompanying ‘checklist’ • The provision of guidance on key issues not yet covered in published guidelines • Consideration of the implications of this work for what might be expected of future models developed as part of the NICE appraisals process

  5. Review of current guidelines • Methods • Systematic search of electronic databases, discussion papers from academic units, internet • Inclusion criteria: • General guidance on the elements of a good model or explicit criteria against which to assess quality of a model • Fifteen papers gave guidance on model quality • Data extracted qualitatively under the general headings: ‘structure’, ‘data’ and ‘consistency’

  6. Key areas of disagreement • Structural issues • The role of data in defining structure • Strategies included in the model • Data issues • Identification of data • Assessment of uncertainty • Consistency • Predictive validity

  7. Development of a best practice guideline • Best practice guidelines for decision modelling for cost-effectiveness analysis • Use themes/evidence identified from previous guidance • Provide a practical framework for critical appraisal: the ‘checklist’ • Assess how comprehensive and useful the ‘checklist’ is

  8. Attributes of good practice: structure • The structure of the model should be consistent with a coherent theory of the health condition under evaluation • The decision problem, scope and objectives of the model should be specified clearly • The model should include all feasible strategies • Structural assumptions transparent and justified

  9. Attributes of good practice: data • Methods for identifying data should be transparent, systematic but not necessarily comprehensive • It should be clear that particular attention has been paid to identifying data for those parameters to which the results of the model are particularly sensitive. • Data incorporated into the model should be described in sufficient detail • In assessing uncertainty, modellers should distinguish between the four principal types of uncertainty.

  10. Attributes of good practice: consistency • The internal consistency of the model (mathematical logic) should be evaluated. • The results of the model should make intuitive sense. • Counter intuitive results should be explained • Data should not be withheld from the model for the purpose of the assessment of external consistency

  11. The value of a ‘checklist’ • Should not be used in isolation or a a ‘points system’ to determine the quality of a model • Can provide general questions to focus a reviewer on the task of critical appraisal and the types of issues that should be considered • Provides a systematic approach to model review • Use in conjunction with general guidelines for economic evaluation • Without knowledge of the disease area it is difficult to assess whether structural assumptions/data used are appropriate

  12. To summarise…. • Attempted to review previous guidance on decision-analytic model quality • Conflicting evidence • Consolidate and update those guidelines • Provide a consensus view • Present an initial framework for the assessment of model quality • Performs well in terms of identifying the aspects of a model that should be of particular concern to the reader • Cannot provide answers as to the appropriateness of structure/ assumptions and data

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