| 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)."
Version: 1.0.0
