Predicting Breast Cancer Incidence Rates Among White and Black Women in the United States: An Application of FTS Model
Development of statistical model for cancer incidence trend predictions can provide a sound and accurate foundation for planning a comprehensive national strategy for optimal partitioning of research resources. Several studies in the past showed that that there are racial/ethnic disparities exist between breast cancer incidence rates among black and white women in the United States. Some of the studies also showed that the disparity in breast cancer incidence rates among white and black US women is widening, with relatively higher incidence rates among black women. In this paper, we apply functional time series (FTS) models on the age-specific breast cancer incidence rates for these two major groups of women in US, and forecast their age-incidence curves. The data are obtained from the Surveillance, Epidemiology and End Results (SEER) program of the United States. We use annual unadjusted breast cancer incidence rates from 1973 to 2013 in 5-year agegroups (15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45–49, 50–54, 55–59, 60–64, 65–69, 70–74, 75–79, 80–84 and 85+). Age-specific cancer incidence curves are obtained using nonparametric smoothing methods. The curves are then decomposed using functional data paradigm and we fit functional time series (FTS) models for each population of women separately. The smoothed incidence curves are then forecasted and prediction intervals are calculated. Fifteen-year forecasts indicate an overall increase in future breast cancer incidence rates for both groups of women. This increase appears to be faster among black women and relatively slower among the whites. The projections suggest a need for equal delivery of quality care to eliminate breast cancer disparities among the two major groups of women in US.
Predicting Breast Cancer Incidence Rates Among White and Black Women in the United States: An Application of FTS Model, International Journal of Statistics and Actuarial Science.
Vol. 1, No. 3,
2017, pp. 68-77.
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