Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees
Authors: Weijia Zhang, Chun Kai Ling, Xuanhui Zhang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments results from a wide range of datasets demonstrate that our approach successfully discerns the underlying dependency structure and significantly reduces survival estimation bias when compared to existing methods. |
| Researcher Affiliation | Academia | 1 School of Information and Physical Sciences, The University of Newcastle, Australia 2 Carnegie Mellon University, USA 3 School of Information Management, Nanjing University, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1https://github.com/Weijia Zhang24/DCSurvival |
| Open Datasets | Yes | We use two semi-synthetic datasets based on the STEEL (V E, Shin, and Cho 2020) which contains 35,040 samples with 9 covariates, and the Airfoil (Thomas Brooks 1989) datasets which includes 1503 samples with 6 covariates. ... The SEER dataset is from the Surveillance, Epidemiology and End Results database (Howlader et al. 2010). ... GBSG2 is from the German Breast Cancer Study Group (Schumacher et al. 1994) |
| Dataset Splits | Yes | We use Adam W (Loshchilov and Hutter 2019) for optimization and use 50%/30%/20% training/validation/test splits. |
| Hardware Specification | Yes | Experiments are conducted with one NVIDIA RTX4090 GPU. |
| Software Dependencies | No | We utilize Pytorch (Paszke et al. 2017) for implementing all neural networks and automatic differentiation. |
| Experiment Setup | Yes | Tensors are computed with double precision (fp64) as the inversion of φ mandates numerical precision. When using Newton s method to compute the inverse φ 1 nn, we terminate when the error is less than 1 10 12. For all our experiments we set φnn with L = 2 and H1 = H2 = 10, i.e., the copula representation contains two hidden layers with each of width 10. ... We use Adam W (Loshchilov and Hutter 2019) for optimization and use 50%/30%/20% training/validation/test splits. We used validation samples for early stopping based on the validation log-likelihood. |