Projections of cancer mortality risks using spatio-temporal P-spline models
Related Experiment Video
Updated: May 22, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
M D Ugarte1, T Goicoa, J Etxeberria
1Department of Statistics and O. R., Public University of Navarre, Spain. lola@unavarra.es
Abstract:
Cancer mortality risk estimates are essential for planning resource allocation and designing and evaluating cancer prevention and management strategies. However, mortality figures generally become available after a few years, making necessary to develop reliable procedures to provide current and near future mortality risks. In this work, a spatio-temporal P-spline model is used to provide predictions of mortality/incidence counts. The model is appropriate to capture smooth temporal trends and to predict cancer mortality/incidence counts in different regions for future years. The prediction mean squared error of the forecast values as well as an appropriate estimator are derived. Spanish prostate cancer mortality data in the period 1975-2008 will be used to illustrate results with a focus on cancer mortality forecasting in 2009-2011.
Related Concept Videos
Cancer Survival Analysis
Survival Curves
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Mouse Models of Cancer Study
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Exponential Equations for Modeling Growth
Introduction To Survival Analysis
The primary goal of survival analysis is to estimate survival time—the time until a...
