Exploring Flexible Penalization of Bayesian Survival Analysis Using Beta Process Prior for Baseline Hazard
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Date
2025-01-21
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Publisher
MDPI
Abstract
High-dimensional data have attracted considerable interest from researchers,
especially in the area of variable selection. However, when dealing with time-to-event
data in survival analysis, where censoring is a key consideration, progress in addressing
this complex problem has remained somewhat limited. Moreover, in microarray research,
it is common to identify groupings of genes involved in the same biological pathways.
These gene groupings frequently collaborate and operate as a unified entity. Therefore, this
study is motivated to adopt the idea of a penalized semi-parametric Bayesian Cox (PSBC)
model through elastic-net and group lasso penalty functions (PSBC-EN and PSBC-GL)
to incorporate the grouping structure of the covariates (genes) and optimally perform
variable selection. The proposed methods assign a beta process prior to the cumulative
baseline hazard function (PSBC-EN-B and PSBC-GL-B), instead of the gamma process
prior used in existing methods (PSBC-EN-G and PSBC-GL-G). Three real-life datasets and
simulation scenarios were considered to compare and validate the efficiency of the modified
methods with existing techniques, using Bayesian information criteria (BIC). The results of
the simulated studies provided empirical evidence that the proposed methods performed
better than the existing methods across a wide range of data scenarios. Similarly, the results
of the real-life study showed that the proposed methods revealed a substantial improvement
over the existing techniques in terms of feature selection and grouping behavior.
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Citation
Dauda, K. A., Adeniyi, E. J., Lamidi, R. K., & Wahab, O. T. (2025). Exploring Flexible Penalization of Bayesian Survival Analysis Using Beta Process Prior for Baseline Hazard. Computation, 13(2), 21. https://doi.org/10.3390/computation13020021