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. Author manuscript; available in PMC: 2020 Jul 21.
Published in final edited form as: Value Health. 2020 Mar;23(3):277–286. doi: 10.1016/j.jval.2020.01.004

Algorithm 1.

Single loop Monte Carlo scheme for computing EVPI

1. Sample a value from the distribution of the uncertain parameters.
2. Evaluate the utility function for each decision option using the parameter values generated in step 1. Store the values.
3. Repeat steps 1 to 2 for N samples (e.g., 10,000). This is the probabilistic analysis sample.
4. Calculate the expected (mean) utility value of the N samples for each decision option.
5. Choose the maximum of the expected utility values in step 4 and store. This is the expected utility with current knowledge.
6. Calculate the maximum utility of the decision options for each of the N samples generated in step 3.
7. Calculate the mean of the N maximum utilities generated in step 6. This is the expected utility when uncertainty is resolved with perfect information.
8. Calculate the EVPI as the difference between the expected utility when uncertainty is resolved with perfect information (step 7) and the expected utility with current knowledge (step 5).