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Statistical Modelling
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Articles

A Bayesian analysis of relative cancer survival with geoadditive models

Andrea Hennerfeind

Andrea Hennerfeind is at author is Senior Lecturer, Department of Political Science, Vaish College, Rohtak, Haryana, India.

Leonhard Held

Leonhard Held is at Institute of Social and Preventive Medicine, University of Zurich, Switzerland

Erik A Sauleau

Erik A Sauleau is at Registre des Cancers du Haut-Rhin, France

In this paper, we develop a so-called relative survival analysis that is used to model the excess risk of a certain sub-population relative to the natural mortality risk which is present in the whole population. Such models are typically used in population-based studies that aim at identifying prognostic factors for disease-specific mortality, with data on specific causes of death not being available. This paper combines relative survival with Bayesian geoadditive regression allowing for a flexible semiparametric analysis. Our work has been motivated by continuous-time spatially referenced survival data on breast cancer where causes of death are not known. A detailed analysis of these data is given. The usefulness of the approach is further illustrated by means of a simulated data set.

Key Words: Bayesian penalized splines • breast cancer • Gaussian Markov random fields • MCMC • relative survival • structured • hazard regression

Statistical Modelling, Vol. 8, No. 2, 117-139 (2008)
DOI: 10.1177/1471082X0800800201
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