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Comparison and multi-model inference of excess risks models for radiation-related solid cancer

In assessments of detrimental health risks from exposures to ionising radiation, many forms of risk to dose–response models are available in the literature. The usual practice is to base risk assessment on one specific model and ignore model uncertainty. The analysis illustrated here considers model...

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Autores principales: Stabilini, Alberto, Hafner, Luana, Walsh, Linda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9950237/
https://www.ncbi.nlm.nih.gov/pubmed/36680572
http://dx.doi.org/10.1007/s00411-022-01013-0
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author Stabilini, Alberto
Hafner, Luana
Walsh, Linda
author_facet Stabilini, Alberto
Hafner, Luana
Walsh, Linda
author_sort Stabilini, Alberto
collection PubMed
description In assessments of detrimental health risks from exposures to ionising radiation, many forms of risk to dose–response models are available in the literature. The usual practice is to base risk assessment on one specific model and ignore model uncertainty. The analysis illustrated here considers model uncertainty for the outcome all solid cancer incidence, when modelled as a function of colon organ dose, using the most recent publicly available data from the Life Span Study on atomic bomb survivors of Japan. Seven recent publications reporting all solid cancer risk models currently deemed plausible by the scientific community have been included in a model averaging procedure so that the main conclusions do not depend on just one type of model. The models have been estimated with different baselines and presented for males and females at various attained ages and ages at exposure, to obtain specially computed model-averaged Excess Relative Risks (ERR) and Excess Absolute Risks (EAR). Monte Carlo simulated estimation of uncertainty on excess risks was accounted for by applying realisations including correlations in the risk model parameters. Three models were found to weight the model-averaged risks most strongly depending on the baseline and information criteria used for the weighting. Fitting all excess risk models with the same baseline, one model dominates for both information criteria considered in this study. Based on the analysis presented here, it is generally recommended to take model uncertainty into account in future risk analyses. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00411-022-01013-0.
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spelling pubmed-99502372023-02-25 Comparison and multi-model inference of excess risks models for radiation-related solid cancer Stabilini, Alberto Hafner, Luana Walsh, Linda Radiat Environ Biophys Original Article In assessments of detrimental health risks from exposures to ionising radiation, many forms of risk to dose–response models are available in the literature. The usual practice is to base risk assessment on one specific model and ignore model uncertainty. The analysis illustrated here considers model uncertainty for the outcome all solid cancer incidence, when modelled as a function of colon organ dose, using the most recent publicly available data from the Life Span Study on atomic bomb survivors of Japan. Seven recent publications reporting all solid cancer risk models currently deemed plausible by the scientific community have been included in a model averaging procedure so that the main conclusions do not depend on just one type of model. The models have been estimated with different baselines and presented for males and females at various attained ages and ages at exposure, to obtain specially computed model-averaged Excess Relative Risks (ERR) and Excess Absolute Risks (EAR). Monte Carlo simulated estimation of uncertainty on excess risks was accounted for by applying realisations including correlations in the risk model parameters. Three models were found to weight the model-averaged risks most strongly depending on the baseline and information criteria used for the weighting. Fitting all excess risk models with the same baseline, one model dominates for both information criteria considered in this study. Based on the analysis presented here, it is generally recommended to take model uncertainty into account in future risk analyses. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00411-022-01013-0. Springer Berlin Heidelberg 2023-01-21 2023 /pmc/articles/PMC9950237/ /pubmed/36680572 http://dx.doi.org/10.1007/s00411-022-01013-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Stabilini, Alberto
Hafner, Luana
Walsh, Linda
Comparison and multi-model inference of excess risks models for radiation-related solid cancer
title Comparison and multi-model inference of excess risks models for radiation-related solid cancer
title_full Comparison and multi-model inference of excess risks models for radiation-related solid cancer
title_fullStr Comparison and multi-model inference of excess risks models for radiation-related solid cancer
title_full_unstemmed Comparison and multi-model inference of excess risks models for radiation-related solid cancer
title_short Comparison and multi-model inference of excess risks models for radiation-related solid cancer
title_sort comparison and multi-model inference of excess risks models for radiation-related solid cancer
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9950237/
https://www.ncbi.nlm.nih.gov/pubmed/36680572
http://dx.doi.org/10.1007/s00411-022-01013-0
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