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BytesAgain β€Ί Skills β€Ί Survival Analysis (KM)
πŸ¦€ ClawHub

Survival Analysis (KM)

by @aipoch-ai

Generates Kaplan-Meier survival curves, calculates survival statistics (log-rank test, median survival time), and estimates hazard ratios for clinical and bi...

Versionv1.0.0
Downloads1,130
Stars⭐ 1
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TERMINAL
clawhub install survival-analysis-km

πŸ“– About This Skill

name: survival-analysis-km description: Generates Kaplan-Meier survival curves, calculates survival statistics (log-rank test, median survival time), and estimates hazard ratios for clinical and biological survival data analysis. Triggered when user requests survival analysis, Kaplan-Meier plots, time-to-event analysis, or asks about survival statistics in biomedical contexts. version: 1.0.0 category: Bioinfo tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06'

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

    | Argument | Description | Required | |----------|-------------|----------| | --input | Input CSV file path | Yes | | --time | Column name for survival time | Yes | | --event | Column name for event indicator (1=event, 0=censored) | Yes | | --group | Column name for grouping variable | Optional | | --output | Output directory for results | Yes | | --conf-level | Confidence level (default: 0.95) | Optional | | --risk-table | Include risk table in plot | Optional |

    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) = Ξ (tᡒ≀t) (1 - dα΅’/nα΅’) - 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) = hβ‚€(t) Γ— exp(β₁X₁ + Ξ²β‚‚Xβ‚‚ + ...) - 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/ folder for:

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

    • Parameters

    | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | --input | str | Required | Input CSV file path | | --time | str | Required | Column name for survival time | | --event | str | Required | | | --group | str | Required | | | --output | str | Required | Output directory for results | | --conf-level | float | 0.95 | | | --risk-table | str | Required | Include risk table in plot | | --figsize | str | '10 | | | --dpi | int | 300 | |

    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

    | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low |

    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 β†’ Expected output 2. Edge Case: Invalid input β†’ Graceful error handling 3. Performance: Large dataset β†’ Acceptable processing time

    Lifecycle Status

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

    πŸ’‘ Examples

    # 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

    • βš™οΈ Configuration

    # Python dependencies
pip install -r requirements.txt