Article Title

Precision Medicine Decision-Making Tool: Generic Cost-Effectiveness Analysis Models

Publication Date



genetics, genomics, economic studies, technology assessment


Background: Economic evaluation is integral to informed health care decision-making worldwide; however, this research is time-consuming and expensive to conduct. It is especially needed in the rapidly growing and changing field of precision medicine in a form that is relevant to national and local decision-makers. Generic economic evaluation models are proposed as a novel approach to address this critical evidence shortage by transparently adapting published country-specific models to make them generalizable by using available research and allowing users to input local values. The purpose of this study is to apply this approach to develop and test a generic pharmacogenomic economic evaluation model.

Methods: A generic cost-effectiveness model case example was developed to evaluate routine genetic testing in an adult patient population to prevent adverse drug reactions using a published country-specific model. A multidisciplinary international team used a consensus approach to comprehensively review and modify the country-specific model to incorporate generalizable assumptions and parameter values based on evidence reviews and user-provided input values to reflect local conditions. The new generic model was transparently documented, tested and validated using input values and models from multiple countries to compare cost-effectiveness results.

Results: Generic-model base case and probabilistic sensitivity analysis cost-effectiveness results were estimated for implementing a pharmacogenomic test versus two other strategies without the test for multiple countries using country-specific input values. These results were compared to country-specific model results for the same input values. The incremental cost-effectiveness ratios for the generic and country-specific models for three countries and three subpopulations in one country were consistent in terms of whether the pharmacogenomic test strategy was cost-effective at the country-specific threshold value. Differences between the generic and country-specific model results were largely explained by differences in model structure and assumptions.

Conclusion: A generic pharmacogenomic cost-effectiveness model enabling use of local input values is feasible and can offer an efficient and timely value-based decision-making tool. Implementing this approach demonstrates that cost-effectiveness analyses can be rapidly performed without extensive training in decision modeling to provide useful evidence for decision-making and facilitate understanding about what conditions can meet cost-effectiveness thresholds.