calc_evppi {dampack}  R Documentation 
evppi
is used to estimate the Expected Value of Partial Perfect
Information (EVPPI) using a linear regression metamodel approach from a
probabilistic sensitivity analysis (PSA) dataset.
calc_evppi(
psa,
wtp,
params = NULL,
outcome = c("nmb", "nhb"),
type = c("gam", "poly"),
poly.order = 2,
k = 1,
pop = 1,
progress = TRUE
)
psa 
object of class psa, produced by 
wtp 
willingnesstopay threshold 
params 
A vector of parameter names to be analyzed in terms of EVPPI. 
outcome 
either net monetary benefit ( 
type 
either generalized additive models ( 
poly.order 
order of the polynomial, if 
k 
basis dimension, if 
pop 
scalar that corresponds to the total population 
progress 

The expected value of partial pefect information (EVPPI) is the expected
value of perfect information from a subset of parameters of interest,
\theta_I
, of a costeffectiveness analysis (CEA) of D
different
strategies with parameters \theta = \{ \theta_I, \theta_C\}
, where
\theta_C
is the set of complimenatry parameters of the CEA. The
function calc_evppi
computes the EVPPI of \theta_I
from a
matrix of net monetary benefits B
of the CEA. Each column of B
corresponds to the net benefit B_d
of strategy d
. The function
calc_evppi
computes the EVPPI using a linear regression metamodel
approach following these steps:
Determine the optimal strategy d^*
from the expected net
benefits \bar{B}
d^* = argmax_{d} \{\bar{B}\}
Compute the opportunity loss for each d
strategy, L_d
L_d = B_d  B_{d^*}
Estimate a linear metamodel for the opportunity loss of each d
strategy, L_d
, by regressing them on the spline basis functions of
\theta_I
, f(\theta_I)
L_d = \beta_0 + f(\theta_I) + \epsilon,
where \epsilon
is the residual term that captures the complementary
parameters \theta_C
and the difference between the original simulation
model and the metamodel.
Compute the EVPPI of \theta_I
using the estimated losses for
each d
strategy, \hat{L}_d
from the linear regression metamodel
and applying the following equation:
EVPPI_{\theta_I} = \frac{1}{K}\sum_{i=1}^{K}\max_d(\hat{L}_d)
The spline model in step 3 is fitted using the 'mgcv' package.
A list containing 1) a data.frame with WTP thresholds and corresponding EVPPIs for the selected parameters and 2) a list of metamodels used to estimate EVPPI for each strategy at each willingness to pay threshold.
Jalal H, AlaridEscudero F. A General Gaussian Approximation Approach for Value of Information Analysis. Med Decis Making. 2018;38(2):174188.
Strong M, Oakley JE, Brennan A. Estimating Multiparameter Partial Expected Value of Perfect Information from a Probabilistic Sensitivity Analysis Sample: A Nonparametric Regression Approach. Med Decis Making. 2014;34(3):311–26.