Set the analysis
Changes the spread of synthetic costs and effects, not their expected values.
More draws reduce visible Monte Carlo noise.
Illustrative alternatives
Standard care is the reference. Programme A has moderate added cost and health gain. Programme B has greater expected cost and health gain.
This seeded synthetic example is for learning. It does not represent clinical or reimbursement evidence.
Probability each alternative is cost-effective
Standard careProgramme AProgramme BSelected threshold
How to interpret the CEAC
Net monetary benefit for option j in simulation s = (threshold × effectjs) − costjs
- At each threshold, the option with the highest simulated net monetary benefit is counted as cost-effective.
- Each curve reports that option's share of wins across all simulations. The probabilities sum to 100% at every threshold.
- The CEAC describes decision uncertainty. It does not show the size of gains or losses when an option wins or loses.
- The decision rule remains to select the option with the highest expected net benefit, not automatically the highest probability of being cost-effective.
- A CEAC does not measure affordability, budget impact, equity, clinical appropriateness or structural uncertainty omitted from the model.