OBJECTIVES: A Markov decision analytic model was developed de novo to compare the economic impact of BRCA testing accuracy on quality adjusted survival and costs in women at high risk of hereditary breast and ovarian cancer (HBOC). This analysis aims to validate the decision analytic model by comparing selected model outputs with model development and non-model development data sources METHODS: Markov model consisted of 7 possible health states: no cancer, early and advanced breast cancer (BC), early and advanced ovarian cancer (OC), simultaneous BC and OC, and death. Model probabilities for developing cancer, its prevalence, and lifetime risk of cancer-related death were populated using estimates from the SEER database and published literature. A microsimulation of 10,000 women was used to estimate the cost-effectiveness from US payer perspective over a woman’s lifetime. The clinical history of each woman, including cancer history and preventative treatments received, was captured. Model validation was based on lifetime prevalence of BC and OC and lifetime risk of cancer-related deaths among all women tested RESULTS: The model predicted a lifetime prevalence of BC as 11% and of OC as 1.7%. These values were consistent with the 12% and 1.3% reported by SEER database for lifetime prevalence of BC and OC, respectively. The model predicted lifetime risk of BC-related deaths as 3.3% and of OC-related death as 1.2%. These values were also consistent with the 2.8% and 1.0% reported by SEER database for lifetime risk of BC-related deaths and OC-related deaths CONCLUSIONS: The model predictions of lifetime prevalence and cancer-related deaths of both BC and OC were similar to estimates reported by SEER database. This suggests that the model could serve as a reliable tool to support decision making based on the success in predicting real-world estimates