Sensitivity analyses (SAs) are an essential tool in health economic analyses. They help explore how uncertainties in input parameters can affect the results. A SA involves varying model parameters across a plausible range and recalculating the outcomes to assess whether conclusions stay the same. Common approaches include scenario analysis, deterministic SA, and probabilistic SA. Scenario analyses often use best-case/worst-case settings or study-specific scenarios. One of the most widely used deterministic SAs is the one-way SA, where only one parameter is changed at a time while the other parameters are kept constant, thereby quantifying the single parameter’s impact on the outcome. This procedure is repeated for all parameters selected for SA. Results are often presented as tornado diagram, which visually ranks the most influencing parameters. Probabilistic SAs are a more complex approach, as the parameters are not assigned fixed values, but distributions, resulting in a range of possible inputs. The model is then run multiple times (e.g., 1000 or 10,000 times) to generate a range of outcomes. This method is called Monte Carlo simulation and results are often displayed as scatterplot, visualizing the results for the repeated simulations.