ArticleThe annals of applied statistics2020
BAYESIAN VARIABLE SELECTION FOR SURVIVAL DATA USING INVERSE MOMENT PRIORS.
Article in The annals of applied statistics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- BAYESIAN VARIABLE SELECTION IN A COX PROPORTIONAL HAZARDS MODEL WITH THE "SUM OF SINGLE EFFECTS" PRIOR.ArXiv · 2025Article
- Incorporation of Epstein-Barr viral variation implicates significance of Latent Membrane Protein 1 in survival prediction and prognostic subgrouping in Burkitt lymphoma.International journal of cancer · 2025Article
- Bayesian Inference for High Dimensional Cox Models with Gaussian and Diffused-Gamma Priors: A Case Study of Mortality in COVID-19 Patients Admitted to the ICU.Statistics in biosciences · 2024Article
- Adaptive MCMC for Bayesian Variable Selection in Generalised Linear Models and Survival Models.Entropy (Basel, Switzerland) · 2023Article
- Two-level Bayesian interaction analysis for survival data incorporating pathway information.Biometrics · 2023Article
- Genetic fine-mapping from summary data using a nonlocal prior improves the detection of multiple causal variants.Bioinformatics (Oxford, England) · 2023Article
- Variational Bayes for high-dimensional proportional hazards models with applications within gene expression.Bioinformatics (Oxford, England) · 2022Article
- Bayesian hierarchical lasso Cox model: A 9-gene prognostic signature for overall survival in gastric cancer in an Asian population.PloS one · 2022Article
- The Application of Bayesian Methods in Cancer Prognosis and Prediction.Cancer genomics & proteomicsReview
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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.
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Registered trials
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