Variational Bayes for High-Dimensional Survival Analysis https://arxiv.org/abs/2112.10270
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Updated
Mar 21, 2022 - C++
Variational Bayes for High-Dimensional Survival Analysis https://arxiv.org/abs/2112.10270
Privacy-preserving survival analysis using multiparty computation
R and C++ code for performing posterior inference for Bayesian Conditional Transformation models illustrated for three different applications.
Code for simulation studies in "An approximate quasi-likelihood approach for error-prone failure time outcomes and exposures", Boe et al. (2021) (https://doi.org/10.1002/sim.9108)
COX PROPORTIONAL HAZARD SURVIVAL REGRESSION ANALYSIS USING THE EFRON METHOD
tracking survival rate of new employees with a best fitted Cox Proportional Hazards model using 4 most significant personality traits
Code for: The Cox-Polya-Gamma Algorithm for Flexible Bayesian Inference of Multilevel Survival Models. Weibull PH model based on Cox-PG algorithm has been added (25JAN2026).
R scripts for preprocessing, pathway scoring, Bayesian modeling, and Cox proportional hazards analyses applied to the SCAPeSCLC dataset, integrating GeoMx Cancer Transcriptome Atlas expression and survival outcomes in ES-SCLC patients treated with chemo-immunotherapy.
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