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Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain

Cancer survival represents one of the main indicators of interest in cancer epidemiology. However, the survival of cancer patients can be affected by several factors, such as comorbidities, that may interact with the cancer biology. Moreover, it is interesting to understand how different cancer site...

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Autores principales: Rubio, Francisco Javier, Alvares, Danilo, Redondo-Sanchez, Daniel, Marcos-Gragera, Rafael, Sánchez, María-José, Luque-Fernandez, Miguel Angel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8978388/
https://www.ncbi.nlm.nih.gov/pubmed/35369875
http://dx.doi.org/10.1186/s12874-022-01582-0
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author Rubio, Francisco Javier
Alvares, Danilo
Redondo-Sanchez, Daniel
Marcos-Gragera, Rafael
Sánchez, María-José
Luque-Fernandez, Miguel Angel
author_facet Rubio, Francisco Javier
Alvares, Danilo
Redondo-Sanchez, Daniel
Marcos-Gragera, Rafael
Sánchez, María-José
Luque-Fernandez, Miguel Angel
author_sort Rubio, Francisco Javier
collection PubMed
description Cancer survival represents one of the main indicators of interest in cancer epidemiology. However, the survival of cancer patients can be affected by several factors, such as comorbidities, that may interact with the cancer biology. Moreover, it is interesting to understand how different cancer sites and tumour stages are affected by different comorbidities. Identifying the comorbidities that affect cancer survival is thus of interest as it can be used to identify factors driving the survival of cancer patients. This information can also be used to identify vulnerable groups of patients with comorbidities that may lead to worst prognosis of cancer. We address these questions and propose a principled selection and evaluation of the effect of comorbidities on the overall survival of cancer patients. In the first step, we apply a Bayesian variable selection method that can be used to identify the comorbidities that predict overall survival. In the second step, we build a general Bayesian survival model that accounts for time-varying effects. In the third step, we derive several posterior predictive measures to quantify the effect of individual comorbidities on the population overall survival. We present applications to data on lung and colorectal cancers from two Spanish population-based cancer registries. The proposed methodology is implemented with a combination of the R-packages mombf and rstan. We provide the code for reproducibility at https://github.com/migariane/BayesVarImpComorbiCancer.
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spelling pubmed-89783882022-04-05 Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain Rubio, Francisco Javier Alvares, Danilo Redondo-Sanchez, Daniel Marcos-Gragera, Rafael Sánchez, María-José Luque-Fernandez, Miguel Angel BMC Med Res Methodol Research Cancer survival represents one of the main indicators of interest in cancer epidemiology. However, the survival of cancer patients can be affected by several factors, such as comorbidities, that may interact with the cancer biology. Moreover, it is interesting to understand how different cancer sites and tumour stages are affected by different comorbidities. Identifying the comorbidities that affect cancer survival is thus of interest as it can be used to identify factors driving the survival of cancer patients. This information can also be used to identify vulnerable groups of patients with comorbidities that may lead to worst prognosis of cancer. We address these questions and propose a principled selection and evaluation of the effect of comorbidities on the overall survival of cancer patients. In the first step, we apply a Bayesian variable selection method that can be used to identify the comorbidities that predict overall survival. In the second step, we build a general Bayesian survival model that accounts for time-varying effects. In the third step, we derive several posterior predictive measures to quantify the effect of individual comorbidities on the population overall survival. We present applications to data on lung and colorectal cancers from two Spanish population-based cancer registries. The proposed methodology is implemented with a combination of the R-packages mombf and rstan. We provide the code for reproducibility at https://github.com/migariane/BayesVarImpComorbiCancer. BioMed Central 2022-04-03 /pmc/articles/PMC8978388/ /pubmed/35369875 http://dx.doi.org/10.1186/s12874-022-01582-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Rubio, Francisco Javier
Alvares, Danilo
Redondo-Sanchez, Daniel
Marcos-Gragera, Rafael
Sánchez, María-José
Luque-Fernandez, Miguel Angel
Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain
title Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain
title_full Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain
title_fullStr Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain
title_full_unstemmed Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain
title_short Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain
title_sort bayesian variable selection and survival modeling: assessing the most important comorbidities that impact lung and colorectal cancer survival in spain
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8978388/
https://www.ncbi.nlm.nih.gov/pubmed/35369875
http://dx.doi.org/10.1186/s12874-022-01582-0
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