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

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; face validity. External validation was assessed in terms of: operational validation; convergent validity (or corroboration); 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 based 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.