Abstract: Breast cancer incidence is increasing among Asian Indian and Pakistani American (AIPA) women. We examined age-specific incidence rate for AIPA women by applying segmented Poisson regression to data from the Surveillance, Epidemiology and End Results (SEER) program between 1990 and 2014, and studied factors associated with breast cancer-specific mortality in AIPA women diagnosed between 2000 and 2016. We used data from non-Hispanic White (NHW) women as a benchmark for comparison. In this talk, I will summarize how we applied the segmented Poisson regression model and models based on sub-distribution and cause-specific hazard functions to assess breast cancer-specific mortality in our study. I will also summarize various features of the R programming language that we used to visualize the data and results, and to conduct parallel computing to optimize the analysis time. Our analyses showed that breast cancer incidence increases rapidly with age in AIPA women, and the rate of increase decreases around age at menopause. While AIPA women have lower hazard for breast cancer-specific death than NHW women, they are followed for a considerably shorter duration. These results provide insights into the kinds of data we need collect to better understand risk and risk factors for breast cancer in AIPA women. I will end the talk with a summary of ongoing data collection efforts through collaborative studies with New Jersey’s South Asian community.