Temporally-Consistent Survival Analysis
Authors: Lucas Maystre, Daniel Russo
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate empirically that it achieves better sample-efficiency and predictive performance compared to approaches that directly regress the observed survival outcome. In this section we evaluate TCSR (Algorithm 1) empirically on synthetic and real-world data. |
| Researcher Affiliation | Collaboration | Lucas Maystre Spotify lucasm@spotify.com Daniel Russo Columbia University & Spotify djr2174@gsb.columbia.edu |
| Pseudocode | Yes | Algorithm 1 Temporally-consistent survival regression (TCSR). Require: dataset of transitions T = {(xm, x m)}, initial parameters θ. 1: repeat 2: for m = 1, . . . , M do 3: ym1 1{x m = } Based on one-step observed outcome. 4: wm1 1 5: for k = 2, . . . , K do Based on predictions at x m. 6: ymk hθ(k 1|x m) 7: wmk Sθ(k 2|x m) 8: θ arg minθ PM m=1 PK k=1 wmk H[ymk hθ(k|xm)] 9: until θ has converged |
| Open Source Code | Yes | We provide a software library with a reference implementation of TCSR in the Python programming language as well as computational notebooks that enable reproducing the results presented in this section at https://github.com/spotify-research/tdsurv. |
| Open Datasets | Yes | Of the three datasets we consider, two contain real data from clinical studies and are publicly available online [30]. The first study tracks survival outcomes from 312 patients diagnosed with PBC, a rare liver disease [25]. The second study follows 467 patients diagnosed with HIV / AIDS [2]. |
| Dataset Splits | Yes | We report the average performance obtained by using 5-fold cross-validation in Figure 2. |
| Hardware Specification | Yes | The experiments were conducted on a desktop computer with a 10th Gen Intel i9-10900X CPU (3.7 GHz) and two NVIDIA GeForce RTX 3090 GPUs. |
| Software Dependencies | Yes | In our implementation of TCSR, we delegate Line 8 in Algorithm 1 to a Newton-CG solver provided by the Sci Py library [31]. The referenced Sci Py version is 1.0. |
| Experiment Setup | Yes | For simplicity, we have fixed the number of iterations to 30 across all datasets and experiments, but the algorithm often converges in fewer iterations. |