Calculate the expected value of perfect information from a decision model using standard Monte Carlo simulation
evpi(outputs, nsim = NULL)The expected value of perfect information, either as a single value, or a data frame indicating the value for each willingness-to-pay.
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.
Users of heemod can create an object of this form, given an object
produced by run_psa (obj, say), with import_heemod_outputs.
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.
Number of simulations from the decision model to use
for calculating EVPPI. The first nsim rows of the
objects in inputs and outputs are used.