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A Bayesian analysis of relative cancer survival with geoadditive models


Hennerfeind, A; Held, L; Sauleau, E A (2008). A Bayesian analysis of relative cancer survival with geoadditive models. Statistical Modelling, 8(2):117-139.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2008
Deposited On:09 Sep 2008 13:29
Last Modified:18 Feb 2018 09:30
Publisher:Sage Publications
ISSN:1471-082X
OA Status:Closed
Publisher DOI:https://doi.org/10.1177/1471082X0800800201

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