Table 2.

Examples of common scenarios for sensitivity analyses in clinical trials

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