ArticleStatistical methods in medical research2017
Weibull regression with Bayesian variable selection to identify prognostic tumour markers of breast cancer survival.
Article in Statistical methods in medical research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Cost-Effectiveness Analysis of Stereotactic Ablative Body Radiotherapy for the Treatment of Oligometastatic Tumors versus Standard of Care.Current oncology (Toronto, Ont.) · 2021Trial
- Flexible yet Sparse Bayesian Survival Models With Time-Varying Coefficients and Unobserved Heterogeneity.Statistics in medicine · 2026Article
- Genome-wide iterative fine-mapping for non-Gaussian phenotypes.Scientific reports · 2025Article
- BAYESIAN VARIABLE SELECTION IN A COX PROPORTIONAL HAZARDS MODEL WITH THE "SUM OF SINGLE EFFECTS" PRIOR.ArXiv · 2025Article
- Group lasso priors for Bayesian accelerated failure time models with left-truncated and interval-censored data.Statistical methods in medical research · 2024Article
- Linked shrinkage to improve estimation of interaction effects in regression models.Epidemiologic methods · 2024Article
- Adaptive MCMC for Bayesian Variable Selection in Generalised Linear Models and Survival Models.Entropy (Basel, Switzerland) · 2023Article
- Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring.Biomedicines · 2023Article
- Controlled variable selection in Weibull mixture cure models for high-dimensional data.Statistics in medicine · 2022Article
- Two-stage penalized regression screening to detect biomarker-treatment interactions in randomized clinical trials.Biometrics · 2022Article
- Recommended Minimal Number of Harvested Lymph Nodes for Intrahepatic Cholangiocarcinoma.Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract · 2021Article
- Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis.Nature communications · 2021Article
- A flexible and parallelizable approach to genome-wide polygenic risk scores.Genetic epidemiology · 2019Article
- Learning from a lot: Empirical Bayes for high-dimensional model-based prediction.Scandinavian journal of statistics, theory and applications · 2019Article
- Fine mapping chromatin contacts in capture Hi-C data.BMC genomics · 2019Article
- Bayesian variable selection for parametric survival model with applications to cancer omics data.Human genomics · 2018Article
- Development and External Validation of Prediction Models for 10-Year Survival of Invasive Breast Cancer. Comparison with PREDICT and CancerMath.Clinical cancer research : an official journal of the American Association for Cancer Research · 2018Article
- A two-step method for variable selection in the analysis of a case-cohort study.International journal of epidemiology · 2018Article
- Analysis of the Factors Affecting the Interval between Blood Donations Using Log-Normal Hazard Model with Gamma Correlated Frailties.Journal of research in health sciences · 2016Article
- The Application of Bayesian Methods in Cancer Prognosis and Prediction.Cancer genomics & proteomicsReview
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7 authors.
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Abstract
As data-rich medical datasets are becoming routinely collected, there is a growing demand for regression methodology that facilitates variable selection over a large number of predictors. Bayesian variable selection algorithms offer an attractive solution, whereby a sparsity inducing prior allows inclusion of sets of predictors simultaneously, leading to adjusted effect estimates and inference of which covariates are most important. We present a new implementation of Bayesian variable selection, based on a Reversible Jump MCMC algorithm, for survival analysis under the Weibull regression model. A realistic simulation study is presented comparing against an alternative LASSO-based variable selection strategy in datasets of up to 20,000 covariates. Across half the scenarios, our new method achieved identical sensitivity and specificity to the LASSO strategy, and a marginal improvement otherwise. Runtimes were comparable for both approaches, taking approximately a day for 20,000 covariates. Subsequently, we present a real data application in which 119 protein-based markers are explored for association with breast cancer survival in a case cohort of 2287 patients with oestrogen receptor-positive disease. Evidence was found for three independent prognostic tumour markers of survival, one of which is novel. Our new approach demonstrated the best specificity.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.