scikit-survival
Category: data-science-and-ml · License: MIT · Version: 1.2 · Includes scripts
Tags: survival analysis, Cox regression, time-to-event, biostatistics, machine learning
When to use it
Builds, evaluates, and audits right-censored survival analysis workflows with scikit-survival (sksurv): Cox PH, Coxnet, IPC ridge, survival trees, forests, boosting, and SVMs, plus nonparametric cumulative incidence for competing risks. Covers leakage-safe scikit-learn pipelines, nested CV, and censoring-aware metrics. Use when fitting survival models on time-to-event data, building structured outcome arrays, computing IPCW concordance, dynamic AUC, or Brier scores, estimating cause-specific cumulative incidence, or tuning models without leakage. Not for Fine-Gray regression, which scikit-survival does not provide.Example requests
Ask your agent in plain language, for example:- “How do I get Uno’s C-index for my Coxnet model in sksurv? I keep getting an error about survival_train.”
- “I have clinic_outcomes.csv with columns age, sex, creatinine, stage, months_to_relapse, relapsed (0/1), and about 8% missing creatinine. I want to compare a random survival forest and a penalized Cox model, tune hyperparameters, and report an honest cross-validated integrated Brier score and time-dependent AUC. Currently I impute and scale the whole table first, then run GridSearchCV. Can you fix this workflow?”
- “My transplant cohort has patients who either relapse, die without relapse, or are censored at last follow-up. I want to estimate the probability of relapse by 3 years without treating death as just censoring. What should I do in Python?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole data-science-and-ml category with /plugin install data-science-and-ml@research-agent-skills.