`evppi`

calculates the expected value of partial perfect information from a decision-analytic model. The default, recommended computation methods are based on nonparametric regression. `evpi`

is also provided for the expected value of perfect information.

`evsi`

calculates the expected value of sample information. Currently this implements the same set of nonparametric regression methods as in `evppi`

, and methods based on moment matching and importance sampling. `enbs`

can then be used to calculate and optimise the expected net benefit of sampling for a simple study with a fixed upfront cost and per-participant costs.

`evppi`

and `evsi`

both require a sample of inputs and outputs from a Monte Carlo probabilistic analysis of a decision-analytic model.

Analogous functions `evppivar`

and `evsivar`

calculate the EVPPI and EVSI for models used for estimation rather than decision-making. The value of information is measured by expected reductions in variance of an uncertain model output of interest.

A pure "brute-force" Monte Carlo method for EVPPI calculation is provided in `evppi_mc`

, though this is usually computationally impractical.

The package overview / Get Started vignette gives worked examples of the use of all of these functions.

## References

Heath, A., Kunst, N., & Jackson, C. (eds.). (2024). Value of Information for Healthcare Decision-Making. CRC Press.

Heath, A., Manolopoulou, I., & Baio, G. (2017). A review of methods for analysis of the expected value of information. Medical Decision Making, 37(7), 747-758.

Heath, A., Kunst, N., Jackson, C., Strong, M., Alarid-Escudero, F., Goldhaber-Fiebert, J. D., Baio, G. Menzies, N.A, Jalal, H. (2020). Calculating the Expected Value of Sample Information in Practice: Considerations from 3 Case Studies. Medical Decision Making, 40(3), 314-326.

Kunst, N., Wilson, E. C., Glynn, D., Alarid-Escudero, F., Baio, G., Brennan, A., Fairley, M., Glynn, D., Goldhaber-Fiebert, J. D., Jackson, C., Jalal, H., Menzies, N. A., Strong, M., Thom, H., Heath, A. (2020). Computing the Expected Value of Sample Information Efficiently: Practical Guidance and Recommendations for Four Model-Based Methods. Value in Health, 3(6), 734-742.

## Author

**Maintainer**: Christopher Jackson chris.jackson@mrc-bsu.cam.ac.uk

Authors:

Anna Heath anna.heath@sickkids.ca

Other contributors:

Gianluca Baio g.baio@ucl.ac.uk (Author of code taken from the BCEA package) [contributor]

Mark Strong mark.strong@sheffield.ac.uk (Author of code taken from the SAVI package) [contributor]

Kofi Placid Adragni (Author of code taken from the ldr package) [contributor]

Andrew Raim (Author of code taken from the ldr package) [contributor]