RT Journal Article
T1 Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain.
A1 Rubio, Francisco Javier
A1 Alvares, Danilo
A1 Redondo-Sanchez, Daniel
A1 Marcos-Gragera, Rafael
A1 Sanchez-Perez, Maria-Jose
A1 Luque-Fernandez, Miguel Angel
K1 Bayesian variable selection
K1 Cancer survival
K1 Comorbidities
K1 Conditional effects
K1 Marginal effects
AB 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 .
PB BioMed Central Ltd.
YR 2022
FD 2022-03-21
LK http://hdl.handle.net/10668/20265
UL http://hdl.handle.net/10668/20265
LA en
NO Rubio FJ, Alvares D, Redondo-Sanchez D, Marcos-Gragera R, Sánchez MJ, Luque-Fernandez MA. Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain. BMC Med Res Methodol. 2022 Apr 3;22(1):95
DS RISalud
RD Apr 10, 2025