Survival Analysis Km

Source: https://github.com/aipoch/medical-research-skills

Survival Analysis (Kaplan-Meier)

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

When to Use

• Use this skill when the task needs Kaplan-Meier survival analysis tool for clinical and biological research. Generates publication-ready survival curves with statistical tests.
• Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

See ## Features above for related details.

• Scope-focused workflow aligned to: Kaplan-Meier survival analysis tool for clinical and biological research. Generates publication-ready survival curves with statistical tests.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• lifelines: Core survival analysis library
• matplotlib, seaborn: Visualization
• pandas, numpy: Data handling
• scipy: Statistical tests

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Data Analytics/survival-analysis-km"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py

# Example invocation: python scripts/main.py --help

# Example invocation: python scripts/main.py --input "Audit validation sample with explicit symptoms, history, assessment, and next-step plan."

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

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

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

Arguments

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

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

Example

# Basic survival curve

# Example invocation: 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