bio-machine-learning-survival-analysis

What problem does it solve?

Analyzes time-to-event data using Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression with lifelines. Builds survival models from clinical and omics features. Use when predicting patient survival or modeling time-to-event outcomes.

Core Features & Use Cases

• Kaplan-Meier estimation and plotting for survival curves across groups.
• Log-rank comparison to assess differences between cohorts.
• Cox proportional hazards regression for univariate and multivariate risk modeling.
• Risk scoring and interpretation of hazard ratios with C-index checks.
• Feature selection for survival analyses in high-dimensional datasets.

Quick Start

Load your time-to-event data and fit a Kaplan-Meier curve, then build a Cox model to estimate hazard ratios.