Skip to main content

Table 2 Examples of common scenarios for sensitivity analyses in clinical trials

From: A tutorial on sensitivity analyses in clinical trials: the what, why, when and how

Scenario Sensitivity analysis options
Outliers - Assess outlier by z-score or boxplot
- Perform analyses with and without outliers
Non-compliance or protocol violation in RCTs Perform
- Intention-to-treat analysis (as primary analysis)
- As-treated analysis
- Per-protocol analysis
Missing data - Analyze only complete cases
- Impute the missing data using single or multiple imputation methods and redo the analysis
Definitions of outcomes - Perform analyses on outcomes of different cut-offs or definitions
Clustering or correlation - Compare the analysis that ignores clustering with one primary method chosen to account for clustering
and multi-center trials
- Compare the analysis that ignores clustering with several methods of accounting for clustering [10, 11]
- Perform analysis with and without adjusting for center
- Use different methods of adjusting for center [12]
Competing risks in RCTs - Perform a survival analysis for each event separately
- Use a proportional sub-distribution hazard model (Fine & Grey approach)
- Fit one model by taking into account all the competing risks together [13]
Baseline imbalance Perform:
- Analysis with and without adjustment for baseline characteristics
- Analysis with different methods of adjusting for baseline imbalance. e.g. Multivariable regression vs. propensity score method
Distributional assumptions Perform analyses under different distributional assumptions
- Different distributions (e.g. Poisson vs. Negative binomial)
- Parametric vs. non-parametric methods
- Classical vs. Bayesian methods
  - Different prior distributions