Reduced Breast and Ovarian Cancer Through Targeted Genetic Testing: Estimates Using the NEEMO Microsimulation Model

Abstract

Background: The effectiveness and cost-effectiveness of genetic testing for hereditary breast and ovarian cancer largely rely on the identification and clinical management of individuals with a pathogenic variant prior to developing cancer. Simulation modelling is commonly utilised to evaluate genetic testing strategies due to its ability to synthesise collections of data and extrapolate over long time periods and large populations. Existing genetic testing simulation models use simplifying assumptions for predictive genetic testing and risk management uptake, which could impact the reliability of their estimates. Our objective was to develop a microsimulation model that accurately reflects ..