Simultaneous Prediction Intervals for Patient-Specific Survival Curves
Authors: Samuel Sokota, Ryan D'Orazio, Khurram Javed, Humza Haider, Russell Greiner
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we consider four survival datasets. The Northern Alberta Cancer Dataset (NACD; 2402 patients, 53 features, 36% censorship), includes patients with many types of cancer: including lung, colorectal, head and neck, esophagus, stomach, etc. The other three datasets are from The Cancer Genome Atlas (TCGA) Research Network [Broad Institute TCGA Genome Data Analysis Center, 2016]: Glioblastoma multiforme (GBM; 592 patients, 12 features, 18% censorship), Rectum adenocarcinoma (READ; 170 patients, 18 features, 84% censorship), and Breast invasive carcinoma (BRCA; 1095 patients, 61 features, 86% censorship). These datasets contain rightcensored patients patients for whom the dataset specifies only a lower-bound on survival time. |
| Researcher Affiliation | Academia | Samuel Sokota , Ryan D Orazio , Khurram Javed , Humza Haider and Russell Greiner Department of Computing Science, University of Alberta {sokota, rdorazio, kjaved, hshaider, rgreiner}@ualberta.ca |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/ssokota/spie. |
| Open Datasets | Yes | The other three datasets are from The Cancer Genome Atlas (TCGA) Research Network [Broad Institute TCGA Genome Data Analysis Center, 2016]: Glioblastoma multiforme (GBM; 592 patients, 12 features, 18% censorship), Rectum adenocarcinoma (READ; 170 patients, 18 features, 84% censorship), and Breast invasive carcinoma (BRCA; 1095 patients, 61 features, 86% censorship). |
| Dataset Splits | Yes | Each dataset was shuffled and divided into a training set and a testing set with a 75/25 split. For its greedy hill climbing procedure, GSPIE divided each test patient s survival curve samples into an optimization set and a validation set with a 50/50 split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU, CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using MTLR and NUTS, but it does not specify software dependencies with version numbers (e.g., specific Python library versions or software package versions) required for replication. |
| Experiment Setup | Yes | We trained MTLR using a regularization factor of 1/2, which was tuned with five-fold cross-validation and N time points (where N is the size of the training set), such that an equal number of events occurs between each two time points, as is recommended by [Yu et al., 2011]. We considered a Bayesian approach we collected 10,000 posterior samples from the posterior p(m | D) using NUTS [Hoffman and Gelman, 2014] and estimated joint prediction intervals for the predictive posterior distribution p(s(x, m) | D) of test set patients. |