Advanced Bayesian Survival Modeling for Lung Adenocarcinoma Prognosis: The afthd R Package and Shiny Application
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Abstract
High-dimensional variable selection in time-to-event analysis is a critical area in biostatistics, especially in the context of complex diseases like lung adenocarcinoma (LUAD). LUAD, the most common subtype of lung cancer, presents unique diagnostic and prognostic challenges due to its molecular and genetic diversity. This study introduces an integrated framework for high-dimensional survival analysis, combining feature selection, advanced survival modeling, and robust missing data handling techniques. We developed the afthd R package, designed specifically for Bayesian survival analysis using the Accelerated Failure Time (AFT) model. This package facilitates efficient variable selection in high-dimensional settings, employing regularized methods such as LASSO and Elastic Net, as well as Bayesian approaches for model stability. An accompanying Shiny web application provides an accessible platform for non-programmers, allowing researchers to perform high-dimensional analysis and view results interactively. Using a LUAD dataset from The Cancer Genome Atlas (TCGA), our results identify key biomarkers associated with patient survival, highlighting the practical utility of this framework in LUAD prognosis. This integrated approach lays the groundwork for more precise prognostic modeling, with potential extensions to other cancers and high-dimensional biomedical datasets.
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