Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks
Authors: Quan Zhang, Mingyuan Zhou
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Illustrative experiments are provided on both synthetic and real datasets, and comparison with various benchmark algorithms for survival analysis with competing risks demonstrates distinguished performance of LDR. In this section, we validate the proposed LDR model by a variety of experiments using both synthetic and real data. |
| Researcher Affiliation | Academia | Quan Zhang Mc Combs School of Business The University of Texas at Austin Austin, TX 78712 quan.zhang@mccombs.utexas.edu Mingyuan Zhou Mc Combs School of Business The University of Texas at Austin Austin, TX 78712 mingyuan.zhou@mccombs.utexas.edu |
| Pseudocode | No | The paper describes the inference methods 'Gibbs sampling' and 'maximum a posteriori (MAP) inference' and states they are detailed in the Appendix, but no explicit pseudocode or algorithm blocks are present in the main document. |
| Open Source Code | Yes | Code for reproducible research is available at https://github.com/zhangquan-ut/Lomax-delegate-racing-for-survival-analysis-with-competing-risks. |
| Open Datasets | Yes | We analyze a microarray gene-expression proļ¬le [48] to assess our model performance on real data. The dataset contains a total of 240 patients with diffuse large B-cell lymphoma (DLBCL). We further analyze a publicly accessible dataset from the Surveillance, Epidemiology, and End Results (SEER) Program of National Cancer Institute [49]. |
| Dataset Splits | No | For synthetic data: 'We simulate 1,000 random observations, and use 800 for training and the remaining 200 for testing.' For DLBCL: 'We use 200 observations for training and the remaining 40 for testing.' For SEER: '80% of observations are used as training and the remaining 20% as testing.' The paper specifies training and testing splits but does not explicitly mention a separate validation split. |
| Hardware Specification | No | The authors acknowledge the support of Award IIS-1812699 from the U.S. National Science Foundation, and the computational support of Texas Advanced Computing Center. (This mentions a computing center but no specific hardware details like CPU/GPU models). |
| Software Dependencies | Yes | pec: Prediction Error Curves for Risk Prediction Models in Survival Analysis, 2017. R package version 2.5.4. risk Regression: Risk Regression Models for Survival Analysis with Competing Risks, 2015. R package version 1.1.7. cmprsk: Subdistribution Analysis of Competing Risks, 2014. R package version 2.2-7. |
| Experiment Setup | No | The paper states that 'Some data description, implementation of benchmark approaches, and experiment settings are deferred to the Appendix for brevity', but does not include specific hyperparameters or system-level training settings in the main text. |