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

• Main Authors: Alvares, D. (Author), Luque-Fernandez, M.A (Author), Marcos-Gragera, R. (Author), Redondo-Sanchez, D. (Author), Rubio, F.J (Author), Sánchez, M.-J (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2022
• Subjects: Bayes theorem; Bayes Theorem; Bayesian variable selection; Cancer survival; Colorectal Neoplasms; colorectal tumor; Comorbidities; Conditional effects; human; Humans; lung; Lung; Marginal effects; reproducibility; Reproducibility of Results; Spain
• Online Access: View Fulltext in Publisher

 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. © 2022, The Author(s).