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.

中文翻译：

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使用逆矩先验的贝叶斯变量选择生存数据

高维癌症基因组研究中有效的变量选择对​​于发现与特定癌症类型相关的基因以及预测对治疗的反应至关重要。在此类研究中，普遍存在被审查的生存数据。在本文中，我们介绍了一种贝叶斯变量选择过程，该过程使用由零点质量和逆矩先验组成的先验混合以及Cox比例风险模型定义的部分似然。该过程在R软件包BVSNLP中实现，该软件包支持并行计算，并使用随机搜索方法探索模型空间。贝叶斯模型平均用于预测。