JSM 2004 - Toronto

Abstract #301968

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Activity Number: 384
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #301968
Title: Predicting U.S. Cancer Mortality Counts Using Semiparametric Bayesian Techniques
Author(s): Kaushik Ghosh*+ and Ram C. Tiwari
Companies: George Washington University and National Cancer Institute
Address: Dept. of Statistics, Washington, DC, 20052,
Keywords: local linear model ; local quadratic model ; Dirichlet processes ; Markov chain Monte Carlo ; health statistics ; time series
Abstract:

We present two models for short-term prediction of the number of deaths that arise from common cancers in the United States. For very short-term projections, we use a local linear model in which the slope of the segment joining consecutive death counts is assumed to be randomly distributed. For slightly longer prediction periods, we use a local quadratic model where the series incorporates a random acceleration term. The slope and acceleration terms are assumed to have nonparametric distributions with Dirichlet Process Prior. Through Markov chain Monte Carlo simulations, we can estimate the mean and variance of the predicted number of deaths. We apply the proposed methods to selected common cancer sites and compare the results with those obtained from the currently available methods. It is seen that the local models are very flexible and result in improved predictions.

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