Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Developer Tools
Survival Analysis (KM)
Generates Kaplan-Meier survival curves, calculates survival statistics (log-rank test, median survival time), and estimates hazard ratios for clinical and bi...
skill openclawclawhub Free
Generates Kaplan-Meier survival curves, calculates survival statistics (log-rank test, median survival time), and estimates hazard ratios for clinical and bi...
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Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
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Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- requirements.txt, SKILL.md, scripts/main.py, references/README.md, references/sample_data.csv
Validation
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- Review SKILL.md after the package is downloaded.
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- AIPOCH-AI
- Source page
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Documentation
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
Python Script
python scripts/main.py --input data.csv --time time_col --event event_col --group group_col --output results/
Arguments
ArgumentDescriptionRequired--inputInput CSV file pathYes--timeColumn name for survival timeYes--eventColumn name for event indicator (1=event, 0=censored)Yes--groupColumn name for grouping variableOptional--outputOutput directory for resultsYes--conf-levelConfidence level (default: 0.95)Optional--risk-tableInclude risk table in plotOptional
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
Statistical Methods
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 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 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/ 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
ParameterTypeDefaultDescription--inputstrRequiredInput CSV file path--timestrRequiredColumn name for survival time--eventstrRequired--groupstrRequired--outputstrRequiredOutput directory for results--conf-levelfloat0.95--risk-tablestrRequiredInclude risk table in plot--figsizestr'10--dpiint300
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 IndicatorAssessmentLevelCode ExecutionPython/R scripts executed locallyMediumNetwork AccessNo external API callsLowFile System AccessRead input files, write output filesMediumInstruction TamperingStandard prompt guidelinesLowData ExposureOutput files saved to workspaceLow
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
Success Metrics
Successfully executes main functionality Output meets quality standards Handles edge cases gracefully Performance is acceptable
Test Cases
Basic Functionality: Standard input → Expected output Edge Case: Invalid input → Graceful error handling 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
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs2 Files1 Scripts
- SKILL.md
- references/README.md
- scripts/main.py
- references/sample_data.csv
- requirements.txt