Document Type
Article
Publication Date
2015
Digital Object Identifier (DOI)
https://doi.org/10.1155/2015/721592
Abstract
Incidence and mortality rates are considered as a guideline for planning public health strategies and allocating resources. We apply functional data analysis techniques to model age-specific brain cancer mortality trend and forecast entire age-specific functions using exponential smoothing state-space models. The age-specific mortality curves are decomposed using principal component analysis and fit functional time series model with basis functions. Nonparametric smoothing methods are used to mitigate the existing randomness in the observed data. We use functional time series model on age-specific brain cancer mortality rates and forecast mortality curves with prediction intervals using exponential smoothing state-space model. We also present a disparity of brain cancer mortality rates among the age groups together with the rate of change of mortality rates. The data were obtained from the Surveillance, Epidemiology and End Results (SEER) program of the United States. The brain cancer mortality rates, classified under International Classification Disease code ICD-O-3, were extracted from SEERStat software.
Rights Information
This work is licensed under a Creative Commons Attribution 3.0 License.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Advances in Epidemiology, v. 2015, art. 721592
Scholar Commons Citation
Pokhrel, Keshav P. and Tsokos, Chris Professor, "Forecasting Age-Specific Brain Cancer Mortality Rates Using Functional Data Analysis Models" (2015). Mathematics and Statistics Faculty Publications. 48.
https://digitalcommons.usf.edu/mth_facpub/48
