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Evaluating space‐time models for short‐term cancer mortality risk predictions in small areas
Author(s) -
Etxeberria Jaione,
Goicoa Tomás,
Ugarte Maria D.,
Militino Ana F.
Publication year - 2014
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201200259
Subject(s) - context (archaeology) , term (time) , cancer incidence , computer science , statistics , cancer , prostate cancer , predictive modelling , econometrics , medicine , mathematics , machine learning , geography , physics , archaeology , quantum mechanics
Current cancer mortality data are available with a delay of roughly three years due to the administrative procedure necessary to create the registries. Therefore, health agencies rely on forecast cancer deaths. In this context, statistical procedures providing mortality/incidence risk predictions for different regions or health areas are very useful. These predictions are essential for defining priorities for cancer prevention and treatment. The main objective of this work is to evaluate the predictive performance of alternative spatio‐temporal models for short‐term cancer risk/counts prediction in small areas. All the models analyzed here are presented under a general‐mixed model framework, providing a unified structure of presentation and facilitating the use of similar tools for computing the prediction mean squared error. Prostate cancer mortality data are used to illustrate the behavior of the different models in Spanish provinces.