Age, period and cohort effects in Bayesian smoothing of spatial cancer survival with geoadditive models

Statistics in Medicine
Erik A SauleauLeonhard Held

Abstract

Based on the example of data on breast cancer survival in a specific area in France, this paper describes a Bayesian approach to analysing individual continuous-time spatially referenced survival data. Starting from the well-known Cox model we develop a geoadditive survival model with a baseline effect, spatial effects, age, period and cohort effects as well as the effect of the number of metastases at the time of diagnosis. Furthermore, we also investigate temporal and spatial variations in the effect of the number of metastases. Our approach is particularly useful since we find clear hints for a violation of the proportional hazards assumption and the existence of different spatial patterns for patients with no, one and more than one metastasis. The reliability of our approach is attested by comparison with a parametric model and with several simulated data sets with known risk profiles.

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Citations

Jun 4, 2013·Spatial and Spatio-temporal Epidemiology·Lung-Chang ChienMario Schootman
May 23, 2014·Statistical Methods in Medical Research·Emanuela DreassiNicola Salvati
Jun 27, 2013·Annals of Oncology : Official Journal of the European Society for Medical Oncology·A GiordanoJ M Reuben

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