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

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