Table of Contents
Advances in Epidemiology
Volume 2015, Article ID 721592, 11 pages
Research Article

Forecasting Age-Specific Brain Cancer Mortality Rates Using Functional Data Analysis Models

1Department of Mathematics and Computer Systems, Mercyhurst University, 501 East 38th Street, Erie, PA 16546, USA
2Department of Mathematics and Statistics, University of South Florida, 4202 E Fowler Avenue, Tampa, FL 33620, USA

Received 30 July 2014; Revised 2 January 2015; Accepted 20 January 2015

Academic Editor: Peter N. Lee

Copyright © 2015 Keshav P. Pokhrel and Chris P. Tsokos. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.