Description Usage Arguments Details Value References Examples
evppi_lrmm
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.
1 2 | evppi_lrmm(nmb = NULL, params = NULL, sel.params = 1, sel.gam = T,
k = NULL, verbose = F)
|
nmb |
Matrix of net monetary benefits (NMB). Each column corresponds to the NMB of a different strategy. |
params |
Vector or matrix of parameters. |
sel.params |
A vector including the column index of parameters for which EVPPI should be estimated. |
sel.gam |
Logical variable indicating if a generalized additive model, GAM, (i.e., a spline model) should be fitted (Default = T). If FALSE, a polynomial of degree k is fitted. |
k |
Scalar with the order of basis functions for the spline model if (sel.gam == T) or the degree of polynomial if (sel.gam != T) |
verbose |
Logical variable indicating if estimation progress should be reported. |
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 evppi_lrmm
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
evppi_lrmm
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.
evppi A numeric vector of size one with the EVPPI of the selected parameters
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## Load mgcv package and matrixStats
library(mgcv)
library(matrixStats)
## Load PSA dataset
data(syndX)
## Net monetary benefit (NMB) matrix
nmb <- syndX[, 5:7]
## Matrix of model parameter inputs values theta
theta <- syndX[, 1:4]
## Optimal strategy (d*) based on the highest expected NMB
d.star <- which.max(colMeans(nmb))
d.star
## Define the Loss matrix
loss <- nmb - nmb[, d.star]
## Estimate EVPPI for parameter 1 (MeanVisitsA)
evppi_lrmm(nmb = nmb, params = theta, sel.params = 1, verbose = TRUE)
|
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