bio-machine-learning-survival-analysis

Cox proportional hazards regression with Python lifelines models time-to-event data to estimate hazard ratios and confidence intervals. The Skill fits univariate and multivariate models, outputs risk scores, and provides concordance index and proportional hazards diagnostics.

Log-rank comparison assesses statistical differences between cohort survival curves. The Skill calculates log-rank tests alongside Kaplan-Meier estimation, enabling you to plot survival curves across groups and determine if significant outcome differences exist.

Kaplan-Meier estimation and plotting for clinical datasets is supported. The Skill generates survival curves across patient groups, calculates survival probabilities over time, and applies to clinical and omics datasets requiring time-to-event outcome visualization.

Univariate and multivariate feature selection for survival analyses in high-dimensional datasets is supported. The Skill processes clinical and omics features, performs Cox regression risk scoring, and outputs hazard ratios to identify significant predictors.

Proportional hazards assumption checking and model fit evaluation use concordance index and PH-check diagnostics. The Skill outputs C-index values and diagnostic tests to validate Cox model assumptions and quantify predictive accuracy.