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Efficient variable selection in high-dimensional cancer genomic studies iscritical for discovering genes associated with specific cancer types and forpredicting response to treatment. Censored survival data is prevalent in suchstudies. In this article we introduce a Bayesian variable selection procedurethat uses a mixture prior composed of a point mass at zero and an inversemoment prior in conjunction with the partial likelihood defined by the Coxproportional hazard model. The procedure is implemented in the R packageBVSNLP, which supports parallel computing and uses a stochastic search methodto explore the model space. Bayesian model averaging is used for prediction.The proposed algorithm provides better performance than other variableselection procedures in simulation studies, and appears to provide moreconsistent variable selection when applied to actual genomic datasets.

Bayesian Variable Selection For Survival Data Using Inverse Moment Priors