survival-epi

What problem does it solve?

This Skill helps you analyze epidemiological time-to-event outcomes, including comparing survival curves, estimating hazard effects, and handling competing risks when multiple causes of event can occur.

Core Features & Use Cases

• Kaplan-Meier + log-rank: Estimate survival curves by exposure group and test differences with log-rank (including multi-group log-rank).
• Cox PH with Schoenfeld residuals: Fit Cox proportional hazards models, estimate hazard ratios, and assess the proportional hazards assumption with scaled Schoenfeld residuals.
• Parametric AFT + competing risks: Support flexible parametric survival via AFT models and competing-risks modeling using CIF estimation (Aalen–Johansen) plus cause-specific hazard comparisons (Fine–Gray concept referenced).

Example use: you have a cohort study where patients may die from the disease or from other causes, and you want to quantify how an exposure changes both overall risk over time and cause-specific incidence.

Quick Start

Use the survival-epi skill to generate synthetic survival data, run Kaplan-Meier curves with a log-rank test between exposure groups, then fit a Cox PH model with proportional-hazards checking.