calc_evppi: Estimation of the Expected Value of Partial Perfect...

In dampack: Decision-Analytic Modeling Package

Estimation of the Expected Value of Partial Perfect Information (EVPPI) using a linear regression metamodel approach

Description

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.

Usage

calc_evppi(
  psa,
  wtp,
  params = NULL,
  outcome = c("nmb", "nhb"),
  type = c("gam", "poly"),
  poly.order = 2,
  k = -1,
  pop = 1,
  progress = TRUE
)

Arguments

psa	object of class psa, produced by make_psa_obj
wtp	willingness-to-pay threshold
params	A vector of parameter names to be analyzed in terms of EVPPI.
outcome	either net monetary benefit ("nmb") or net health benefit ("nhb")
type	either generalized additive models ("gam") or polynomial models ("poly")
poly.order	order of the polynomial, if type == "poly"
k	basis dimension, if type == "gam"
pop	scalar that corresponds to the total population
progress	TRUE or FALSE for whether or not function progress should be displayed in console.

Details

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:

1. Determine the optimal strategy d^* from the expected net benefits \bar{B}

   d^* = argmax_{d} \{\bar{B}\}

2. Compute the opportunity loss for each d strategy, L_d

   L_d = B_d - B_{d^*}

3. 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.

4. 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.

Value

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.

References

1. Jalal H, Alarid-Escudero F. A General Gaussian Approximation Approach for Value of Information Analysis. Med Decis Making. 2018;38(2):174-188.

2. 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.

dampack documentation built on Sept. 30, 2024, 5:07 p.m.