Bayesian estimation of parametric AFT models for high-dimensional survival data

There are unique challenges when analyzing high-dimensional survival data. The Accelerated Failure Time (AFT) model serves as a viable alternative to the Cox Proportional Hazards (CPH) model in such settings. Although the AFT model holds significant role, there is a lack of effective tools for its application to high-dimensional survival data, particularly within a Bayesian framework. This work aims to bridge that gap by proposing a comprehensive and practical solution. We use afthd, an R package designed to fit parametric AFT models to high-dimensional survival data. The package supports both frequentist and Bayesian inference and accommodates univariate and multivariable analyses. The results of simulation studies demonstrate that the parameter estimates are robust and consistent in various model specifications. The effectiveness and clarity of the proposed method are further illustrated through its application to two real high-dimensional liver cancer datasets.