sensitivity-analysis

name	sensitivity-analysis
description	Conduct sensitivity analyses to test robustness of findings. Use when: (1) Testing assumption violations, (2) Meta-analysis robustness, (3) Handling missing data, (4) Examining outliers.
allowed-tools	Read, Write, Bash
version	1.0.0

Sensitivity Analysis Skill

Purpose

Test whether findings are robust to analytical decisions and assumptions.

Types of Sensitivity Analyses

1. Exclusion Analyses

• Remove outliers
• Remove high risk-of-bias studies
• One-study-removed analysis

2. Analytical Decisions

• Different statistical tests
• Parametric vs non-parametric
• Different transformations

3. Missing Data

• Complete case analysis
• Best-case scenario
• Worst-case scenario
• Multiple imputation

4. Measurement

• Different outcome definitions
• Different time points
• Alternative scoring methods

Interpretation

Robust Findings:

• Results consistent across analyses
• Conclusions unchanged
• High confidence

Sensitive Findings:

• Results vary by decision
• Interpret with caution
• Report uncertainty

Example

"Results were robust to removal of the highest risk-of-bias study (d=0.48 vs d=0.52) and remained significant when using non-parametric tests (p=.002)."

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Version: 1.0.0