Calculate the expected value of partial perfect information for an estimation problem. This computes the expected reduction in variance in some quantity of interest with perfect information about a parameter or parameters of interest.

evppivar.remote(
  outputs,
  inputs,
  pars = NULL,
  method = NULL,
  nsim = NULL,
  verbose = TRUE,
  ...
)

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.
inputs	Matrix or data frame of samples from the uncertainty distribution of the input parameters of the decision model. The number of columns should equal the number of parameters, and the columns should be named. This should have the same number of rows as there are samples in `outputs`, and each row of the samples in `outputs` should give the model output evaluated at the corresponding parameters.
pars	A character vector giving the parameters of interest, for which a single EVPPI calculation is required. If the vector has multiple element, then the joint expected value of perfect information on all these parameters together is calculated. Alternatively, `pars` may be a list. Multiple EVPPI calculations are then performed, one for each component of `pars` defined in the above vector form. `pars` must be specified if `inputs` is a matrix or data frame. This should then correspond to particular columns of `inputs`. If `inputs` is a vector, this is assumed to define the single parameter of interest, then `pars` is not required.
method	Character string indicating the calculation method. The default methods are based on nonparametric regression
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.
verbose	If `TRUE`, then messages are printed describing each step of the calculation, if the method supplies these. Useful to see the progress of slow calculations.
...	Other arguments to control specific methods.

Value

expected reduction in variance in some quantity of interest with perfect information about a parameter or parameters of interest

Examples

p1 <- rbeta(10000, 5, 95)
beta <- rnorm(10000, 0.8, 0.4)
p2 <- plogis(qlogis(p1) + beta)
inputs <- data.frame(p1, beta)
evppivar.remote(p2, inputs, par="p1")
#> Calling server at https://prism.peermodelsnetwork.com/route/voi/run
#> $pars
#> [1] "p1"
#> 
#> $evppi
#> [1] 0.0019
#>