Abstract #301539

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JSM 2003 Abstract #301539
Activity Number: 128
Type: Contributed
Date/Time: Monday, August 4, 2003 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #301539
Title: Prediction of U.S. Cancer Mortality Counts Using Semiparametric Bayesian Techniques
Author(s): Kaushik Ghosh*+ and Ram C. Tiwari
Companies: George Washington University and National Institutes of Health
Address: Dept. of Statistics, Washington, DC, 20052-0001,
Keywords: Poisson regression ; Dirichlet process prior ; semiparametric Bayes ; MCMC ; cancer mortality
Abstract:

We present a regression model to predict the future number of cancer deaths in the U.S. The number of deaths at any point of time is modeled as a Poisson random variable with mean proportional to the number of persons at risk of death. The logit-transform of the rate of death is assumed to be linear in time, with independent Dirichlet process priors for the slope and intercept parameters to allow for uncertainty in model specification. MCMC techniques are used to predict the number of deaths at a future point in time. The predictions from the new method are compared to those from the method currently used by the American Cancer Society and a recently proposed state-space model. Results of runs on various cancer sites at the national level are presented to illustrate our findings.

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