Bayesian Survival Models for High-Dimensional Data
BayesSurvive: Bayesian Survival Models for High-Dimensional Data
Fit Bayesian Cox Models
Create a dataframe of estimated coefficients
Function to learn MRF graph
Function to run MCMC sampling
Create a plot of estimated coefficients
Time-dependent Brier scores
Predict survival risk
Subfunctions to update parameters
Update coefficients of Bayesian Cox Models
Function to perform variable selection
An implementation of Bayesian survival models with graph-structured selection priors for sparse identification of omics features predictive of survival (Madjar et al., 2021 <doi:10.1186/s12859-021-04483-z>) and its extension to use a fixed graph via a Markov Random Field (MRF) prior for capturing known structure of omics features, e.g. disease-specific pathways from the Kyoto Encyclopedia of Genes and Genomes database (Hermansen et al., 2025 <doi:10.48550/arXiv.2503.13078>).
Useful links