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
)object of class psa, produced by make_psa_obj
willingness-to-pay threshold
A vector of parameter names to be analyzed in terms of EVPPI.
either net monetary benefit ("nmb")
or net health benefit ("nhb")
either generalized additive models ("gam") or
polynomial models ("poly")
order of the polynomial, if type == "poly"
basis dimension, if type == "gam"
scalar that corresponds to the total population
TRUE or FALSE for whether or not function progress
should be displayed in console.
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.
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 cost-effectiveness 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.
Jalal H, Alarid-Escudero F. A General Gaussian Approximation Approach for Value of Information Analysis. Med Decis Making. 2018;38(2):174-188.
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<U+2013>26.