Fit survival models and evaluate censored data with scikit-survival.
npx skills add https://github.com/yf8578/clawomics --skill scikit-survival-yf8578
Please help me install this Agent Skill. Skill: scikit-survival Source: https://github.com/yf8578/clawomics/tree/main/skills/scikit-survival Command: npx skills add https://github.com/yf8578/clawomics --skill scikit-survival-yf8578
This Skill empowers users to perform advanced survival analysis and time-to-event modeling, enabling deeper insights into time-dependent data and patient outcomes.
Use the scikit-survival skill to fit a Cox proportional hazards model to the provided survival data.
High-intent search queries and answers about installing and using this skill.
You can perform survival analysis on censored data by fitting models like Cox proportional hazards or Random Survival Forests to time-to-event data, handling right-censored observations to predict outcomes and identify prognostic factors.
The best way to evaluate time-to-event model performance is using the concordance index and Brier score, which are robust metrics designed specifically to assess prediction accuracy on censored survival data.
Yes, Random Survival Forests and Gradient Boosting can be used for biostatistics modeling to analyze complex time-to-event data, capturing non-linear relationships while properly accommodating censored observations.
Fitting a Cox proportional hazards model to patient data requires preprocessing the survival data to structure censoring correctly, then applying the algorithm to estimate covariate effects on survival time.
You need penalized Cox models for survival analysis when dealing with high-dimensional data or multicollinearity, applying regularization to prevent overfitting and improve model variable selection stability.
Use Survival SVMs instead of Cox models when the proportional hazards assumption is violated, as Support Vector Machines provide flexible ranking alternatives for survival times without relying on strict proportional hazard constraints.
jasrajtulsiAnalyze censored survival datasets and estimate time-to-event risk with scikit-survival models.
ovachieverPerform survival analysis on censored data with Cox models and ensemble methods.
pur3v4d3rTrain and evaluate classical machine learning models with scikit-learn.
sencersoyluEstimate survival curves, compare groups, and model hazards with Kaplan-Meier, log-rank, and Cox methods.
