Survival Analysis (Kaplan-Meier)

Kaplan-Meier survival analysis tool for clinical and biological research. Generates publication-ready survival curves with statistical tests.

Features

• Kaplan-Meier Curve Generation: Publication-quality survival plots with confidence intervals
• Statistical Tests: Log-rank test, Wilcoxon test, Peto-Peto test
• Hazard Ratios: Cox proportional hazards regression with 95% CI
• Summary Statistics: Median survival time, restricted mean survival time (RMST)
• Multi-group Analysis: Supports 2+ comparison groups
• Risk Tables: Optional at-risk table below curves

Usage

Python Script

python scripts/main.py --input data.csv --time time_col --event event_col --group group_col --output results/

Arguments

--input

--time

--event

--group

--output

--conf-level

--risk-table

Input Format

CSV with columns:

• Time column: Numeric, time to event or censoring
• Event column: Binary (1 = event occurred, 0 = censored/right-censored)
• Group column: Categorical variable for stratification

Example:

patient_id,time_months,death,treatment_group
P001,24.5,1,Drug_A
P002,36.2,0,Drug_A
P003,18.7,1,Placebo

Output Files

• km_curve.png

  : Kaplan-Meier survival curve

• km_curve.pdf

  : Vector version for publications

• survival_stats.csv

  : Statistical summary (median survival, confidence intervals)

• hazard_ratios.csv

  : Cox regression results with HR and 95% CI
• `logrank_test.csv**: Pairwise comparison p-values
• `report.txt**: Human-readable summary report

Technical Details

Statistical Methods

1. Kaplan-Meier Estimator: Non-parametric maximum likelihood estimate of survival function

   • Product-limit estimator: Ŝ(t) = Π(tᵢ≤t) (1 - dᵢ/nᵢ)
   • Greenwood's formula for variance estimation

2. Log-Rank Test: Most widely used test for comparing survival curves

   • Null hypothesis: No difference between groups
   • Weighted by number at risk at each event time

3. Cox Proportional Hazards: Semi-parametric regression model

   • h(t|X) = h₀(t) × exp(β₁X₁ + β₂X₂ + ...)
   • Proportional hazards assumption checked via Schoenfeld residuals

Dependencies

• lifelines

  : Core survival analysis library

• matplotlib

  ,

  seaborn

  : Visualization

• pandas

  ,

  numpy

  : Data handling

• scipy

  : Statistical tests

Technical Difficulty: High ⚠️

This skill involves advanced statistical modeling. Results should be reviewed by a biostatistician, especially for:

• Proportional hazards assumption violations
• Small sample sizes (< 30 per group)
• Heavy censoring (> 50%)
• Time-varying covariates

References

See

references/

• Kaplan EL, Meier P (1958) original paper
• Cox DR (1972) regression models paper
• Sample datasets for testing
• Clinical reporting guidelines (ATN, CONSORT)

Parameters

--input

--time

--event

--group

--output

--conf-level

--risk-table

--figsize

--dpi

Example

# Basic survival curve
python scripts/main.py \
  --input clinical_data.csv \
  --time overall_survival_months \
  --event death \
  --group treatment_arm \
  --output ./results/ \
  --risk-table

Output includes:

• Survival curves with 95% confidence bands
• Median survival: Drug A = 28.4 months (95% CI: 24.1-32.7), Placebo = 18.2 months (95% CI: 15.3-21.1)
• Log-rank test p-value: 0.0023
• Hazard ratio: 0.62 (95% CI: 0.45-0.85), p = 0.003

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support