Tuesday, Aug 6: 10:30 AM - 12:20 PM
Contributed Posters
Oregon Convention Center
The amount of available covariates in medical data is expanding with each passing year, making identification of the most influential factors pivotal in survival regression modeling. Bayesian analyses focusing on variable selection are a common approach towards this problem. However, most use approximations of the posterior to perform this task. In this paper, we propose placing a beta prior directly on the model coefficient of determination (Bayesian R2), which acts as a shrinkage prior on the global variance of the predictors. Through reparameterization using an auxiliary variable, we are able to update a majority of the parameters with sequential Gibbs sampling, thus reducing reliance on approximate posterior inference and simplifying computation. Performance over competing variable selection priors is then showcased through an extensive simulation study in both censored and non-censored settings. Finally, the method is applied to identifying influential built environment risk factors impacting survival time of Medicare eligible patients in California with cardiovascular ailments.
Survival
AFT
Global-Local
Bayesian