What is the primary purpose of probabilistic sensitivity analysis (PSA)?

• A. To generate random results with no theoretical basis.
• B. To quantify uncertainty in model inputs by generating a distribution of outcomes.
• C. To select the cheapest option without considering uncertainty.
• D. To create a budget forecast.

Answer: (B)

Probabilistic sensitivity analysis is about showing how uncertainty in model inputs affects the results. Instead of using single fixed numbers for costs, effects, and probabilities, PSA assigns probability distributions to these parameters and then repeatedly samples from those distributions, running the model many times. Each run produces a possible outcome, and putting all those runs together gives a distribution of costs, effects, and derived measures like the incremental cost-effectiveness ratio. This lets you see not just a single estimate but how confident you can be in that estimate, such as the probability that an intervention is cost-effective at a given willingness-to-pay threshold. It also helps identify which inputs contribute most to uncertainty. In short, PSA quantifies uncertainty by generating a range of plausible outcomes rather than a single point result.