OBJECTIVES: Genetic screening identifies candidates for intensified cancer screening and prevention. Due to the high cost of genetic testing, it is important to identify patients who are most likely to benefit. Doing so using clinical trials is prohibitively expensive; thus a mathematical modeling approach is required. METHODS: We developed a framework for  stratified cost-effectiveness analysis using individual-based discrete-event simulations, consisting of a natural history component that captures mutation distribution, correlations between mutation and other risk factors (e.g. family history), and cancer incidence,  progression and mortality, and a healthcare process component that captures interactions between the patients and the healthcare system, through genetic testing, screening, diagnosis and treatment, and their costs. The genetic screening strategy consists of 3 steps: a benefit- risk assessment step, in which patients are assessed for risk of carrying mutations and potential benefits from genetic testing, a genetic testing step, in which qualified patients within an optimal risk bracket are given the appropriate tests and an intervention step, in which patients are given care based on the results from the genetic tests. RESULTS: We use the following approach to explore and identify optimal strategies for 3 genetic screening applications: Dinh et al. demonstrated that primary screening for Lynch syndrome in patients at least 25 years old and with a risk of at least 5% was cost effective. Folse et al. showed that single-nucleotide polymorphism (SNP) screening for breast cancer risk for recommending patients to MRI screening was most cost-effective in women age 40 with a lifetime risk of 16 to 28%. Green et al showed that the same genetic test for recommending patients to chemoprevention was most cost-effective for women age 50-59 with a 5-year risk of 1.2-1.66%. CONCLUSIONS: As more genetic tests becomes available, this method can be used to identify screening strategies that maximize cost-effectiveness.