Calculate the expected value of perfect information from a decision model

Calculate the expected value of perfect information from a decision model

evpi.remote(outputs, nsim = NULL)

Arguments

outputs

This could take one of two forms "net benefit" form: a matrix or data frame of samples from the uncertainty distribution of the expected net benefit. The number of rows should equal the number of samples, and the number of columns should equal the number of decision options. "cost-effectiveness analysis" form: a list with the following named components: "c": a matrix or data frame of samples from the distribution of costs. There should be one column for each decision option. "e": a matrix or data frame of samples from the distribution of effects, likewise. "k": a vector of willingness-to-pay values. Objects of class "bcea", as created by the BCEA package, are in this "cost-effectiveness analysis" format, therefore they may be supplied as the outputs argument. If outputs is a matrix or data frame it is assumed to be of "net benefit" form. Otherwise if it is a list, it is assumed to be of "cost effectiveness analysis" form.

nsim

Number of simulations from the model to use for calculating EVPPI. The first nsim rows of the objects in inputs and outputs are used.

Value

The expected value of perfect information, either as a single value, or a data frame indicating the value for each willingness-to-pay.

Examples

set.seed(1)
nsam <- 10000
inputs <- data.frame(
  p1 = rnorm(nsam, 1, 1),
  p2 = rnorm(nsam, 0, 2)
)
outputs_nb <- data.frame(
  t1 = 0,
  t2 = inputs$p1 - inputs$p2
)
evpi.remote(outputs = outputs_nb)
#> Calling server at https://prism.peermodelsnetwork.com/route/voi/run
#>       [,1]
#> [1,] 0.476