Estimation of the Expected Value of Partial Perfect Information (EVPPI) using a linear regression metamodel approach
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 |
willingness-to-pay 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,
θ_I, of a cost-effectiveness analysis (CEA) of D different
strategies with parameters θ = \{ θ_I, θ_C\}, where
θ_C is the set of complimenatry parameters of the CEA. The
function calc_evppi computes the EVPPI of θ_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 θ_I, f(θ_I)
L_d = β_0 + f(θ_I) + ε,
where ε is the residual term that captures the complementary parameters θ_C and the difference between the original simulation model and the metamodel.
Compute the EVPPI of θ_I using the estimated losses for each d strategy, \hat{L}_d from the linear regression metamodel and applying the following equation:
EVPPI_{θ_I} = \frac{1}{K}∑_{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, 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):311–26.
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