  Efficient variable selection in high-dimensional cancer genomic studies is
critical for discovering genes associated with specific cancer types and for
predicting response to treatment. Censored survival data is prevalent in such
studies. In this article we introduce a Bayesian variable selection procedure
that uses a mixture prior composed of a point mass at zero and an inverse
moment prior in conjunction with the partial likelihood defined by the Cox
proportional hazard model. The procedure is implemented in the R package
BVSNLP, which supports parallel computing and uses a stochastic search method
to explore the model space. Bayesian model averaging is used for prediction.
The proposed algorithm provides better performance than other variable
selection procedures in simulation studies, and appears to provide more
consistent variable selection when applied to actual genomic datasets.
