Presentation
Dynamic Bayesian models for projecting cancer incidence in Puerto Rico
Autor
Pericchi Guerra, Luis R.
Torres, David
Institución
Resumen
We estimate the present (2010) and predict the future (2014) of
incidence for the top cancer tumor types in Puerto Rico (PR), by
gender, age group and primary cancer site, to design public policy.
Incidence data from Puerto Rico Central Cancer Registry were
obtained for the years 1985 to 2004. The dynamic autoregressive
models used in modern epidemiology are function of
age-period-cohort (APC).
We introduce a novel robust and stable prior the autoregressive
variance, the scaled beta prior of the second kind (Beta2 prior).
We found that this leads to a stable convergence of the model at the
Markov Chain Monte Carlo (MCMC) implementation.
We also produce statistical tools to check the goodness of fit and
model selection. 1) Comprehensive Cancer Center of the University of Puerto Rico; 2) NIH; 3) MERCK; 4) College of Natural Sciences