sensitivity-analysis
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.
SKILL.md
Full skill instructions
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)."
Version: 1.0.0