A structured process for the validation of a decision-analytic model: application to a cost-effectiveness model for risk-stratified national breast screening

Stuart J Wright; Ewan Gray; Gabriel Rogers; Anna Donten; Katherine Payne

doi:10.1007/s40258-024-00887-z

A structured process for the validation of a decision-analytic model: application to a cost-effectiveness model for risk-stratified national breast screening

Appl Health Econ Health Policy. 2024 May 16. doi: 10.1007/s40258-024-00887-z. Online ahead of print.

Authors

Stuart J Wright¹, Ewan Gray², Gabriel Rogers³, Anna Donten³, Katherine Payne³

Affiliations

¹ Division of Population Health, Health Services Research and Primary Care, Manchester Centre for Health Economics, The University of Manchester, Oxford Road, Manchester, M139PL, UK. stuart.j.wright@manchester.ac.uk.
² GRAIL, New Penderel House 4th Floor, 283-288 High Holborn, London, WC1V 7HP, UK.
³ Division of Population Health, Health Services Research and Primary Care, Manchester Centre for Health Economics, The University of Manchester, Oxford Road, Manchester, M139PL, UK.

PMID: 38755403
DOI: 10.1007/s40258-024-00887-z

Abstract

Background: Decision-makers require knowledge of the strengths and weaknesses of decision-analytic models used to evaluate healthcare interventions to be able to confidently use the results of such models to inform policy. A number of aspects of model validity have previously been described, but no systematic approach to assessing the validity of a model has been proposed. This study aimed to consolidate the different aspects of model validity into a step-by-step approach to assessing the strengths and weaknesses of a decision-analytic model.

Methods: A pre-defined set of steps were used to conduct the validation process of an exemplar early decision-analytic-model-based cost-effectiveness analysis of a risk-stratified national breast cancer screening programme [UK healthcare perspective; lifetime horizon; costs (£; 2021)]. Internal validation was assessed in terms of descriptive validity, technical validity and face validity. External validation was assessed in terms of operational validation, convergent validity (or corroboration) and predictive validity.

Results: The results outline the findings of each step of internal and external validation of the early decision-analytic-model and present the validated model (called 'MANC-RISK-SCREEN'). The positive aspects in terms of meeting internal validation requirements are shown together with the remaining limitations of MANC-RISK-SCREEN.

Conclusion: Following a transparent and structured validation process, MANC-RISK-SCREEN has been shown to have satisfactory internal and external validity for use in informing resource allocation decision-making. We suggest that MANC-RISK-SCREEN can be used to assess the cost-effectiveness of exemplars of risk-stratified national breast cancer screening programmes (NBSP) from the UK perspective.

Implications: A step-by-step process for conducting the validation of a decision-analytic model was developed for future use by health economists. Using this approach may help researchers to fully demonstrate the strengths and limitations of their model to decision-makers.

Abstract

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