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

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

 

Z Philips,1 L Ginnelly,1* M Sculpher,1 K Claxton,1,2 S Golder,3 R Riemsma,3 N Woolacott3 and J Glanville3

1 Centre for Health Economics, University of York, UK

2 Department of Economics, University of York, UK

3 Centre for Reviews and Dissemination, University of York, UK

Health Technology Assessment 2004; Vol. 8: No. 36

 

Available online PDF [179p.] at: http://www.hta.ac.uk/fullmono/mon836.pdf

 

Objectives: To identify existing guidelines and develop a synthesised guideline plus accompanying checklist. In addition to provide guidance on key theoretical, methodological and practical issues and consider the implications of this research for what might be expected of future decision-analytic models.

Data sources: Electronic databases.

Review methods: A systematic review of existing good practice guidelines was undertaken to identify and summarise guidelines currently available for assessing

the quality of decision-analytic models that have been undertaken for health technology assessment. A synthesised good practice guidance and accompanying

checklist was developed. Two specific methods areas in decision modelling were considered. The first method’s topic is the identification of parameter estimates from published literature. Parameter searches were developed and piloted using a case-study model.

 

The second topic relates to bias in parameter estimates; that is, how to adjust estimates of treatment effect from observational studies where there are risks of selection bias. A systematic literature review was conducted to identify those studies looking at quantification of bias in parameter estimates and the implication of this bias.

 

Content:

 

Executive summary

1 Introduction

2 A framework for good practice in decision analytic modelling studies: review of current guidelines

3 Development of a best practice guideline

Developing the synthesised guidance

Statements of good practice

Applying the guidance: the use of case studies

4 Key methodological and practical issues not covered in existing published guidelines

Appropriate methods for the identification and quality assessment of secondary parameter estimates such as utilities, costs, incidence and prevalence

Effects of selection bias on treatment outcomes

5 Discussion

Review of guidelines for quality assessment in decision models

Methods in decision modelling

Implications for the NICE technology assessment and appraisal process

Recommendations for research

 

References

 

Appendix 1 Search strategies for guidelines of good practice in decision modelling

Appendix 2 Summary of guidelines available in structured format

Appendix 3 Quality assessment in decision-analytic models: a suggested checklist

Appendix 4 Application of the checklist to three decision models

Appendix 5 Members of the Expert

Appendix 6 Comments from the Expert Advisory Group and amended checklist

Appendix 7 Bibliography

Appendix 8 Search strategies and results of the case study

Appendix 9 Searches for the effects

 

 

 

 

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