Method:
Expected Value of Partial Perfect Information (EVPPI) provides an upper limit for the expected gains from carrying out further research to provide information on a focal subset of parameters in a cost-effectiveness model. Calculating EVPPI requires the estimation of an inner expected net benefit over the remaining (non-focal) parameters conditional on the focal parameters. This expectation is nested within an outer expectation over the focal parameters. Since the inner expectation can only be replaced by the unconditional means of the non-focal parameters in special cases, a common general approach is to use nested Monte Carlo simulation to obtain an estimate of EVPPI. This approach is computationally intensive, can lead to significant sampling bias if an inadequate number of inner samples are obtained, and incorrect results can be obtained if correlations between parameters are not dealt with appropriately.We set out a range of methods for estimating EVPPI that avoid the need for nested simulation: reparameterisation of the net benefit function, Taylor series approximations, and restricted cubic spline estimation of conditional expectations.
Result: For each method, we set out the generalised functional form which net benefit must take for the method to be valid. By specifying this functional form, our methods are able to focus on components of the model where approximation is required, avoiding the complexities involved in developing statistical approximations for the model as a whole. Our methods also allow for any correlations that might exist between model parameters. We illustrate the methods using an example of fluid resuscitation in African children with severe malaria.
Conclusion: Careful consideration of the functional form of the net benefit function can allow EVPPI to be calculated in a single step in a wide range of situations. Where EVPPI can be calculated in a single step, avoiding nested Monte Carlo Simulation, this leads to marked improvements in speed and accuracy.