ICEpref: Compute ICE Economic Preference Scores from Numeric Cost and Effe Measures

Description

This function calculates Incremental Net Monetary Benefit (INMB) Scores that are expressed in Cost units. While these scores are of "linear" form, (lambda * Effe) - Cost, when eta == gamma / beta == 1, they are "Nonlinear" when eta is either > 1 or < 1.

Usage

ICEpref(tr, ex, cy, lambda = 1, beta = 1, eta = 3 + 2 * sqrt(2))

Arguments

Binary [0 or 1] indicator of Treatment Regimen received by each patient [Experimental Unit]: tr == 0 denotes the "Standard" Regimen, tr == 1 denotes a "New" Regimen.

Numeric vector representing Effe measures observed for 2 or more experimental units.

Corresponding numeric vector reporting Total Cost Incurred by patients during treatment. The tr, ex and cy vectors must be of the same length, n >= 2, and 1 < sum(tr) < n.

lambda

Positive value for the fair, full-retail Shadow Price of Health. When ex is measured in QALYs and cy in U.S. Dollars, lambda = 50000 is traditionally assumed appropriate.

beta

Positive Returns-to-Scale Power parameter for ICE Preference calculations. beta = 1 implies Linear (Constant) Returns-to-Scale. A beta > 0 but < 1 implies Returns-to-Scale that are Diminishing and tend to be highly Realistic. Any beta > 1 implies Increasing Returns-to-Scale.

eta

Positive Power Parameter Ratio: gamma / beta. Generalized "linear" maps result when eta = 1. An eta ratio > 1 can yield a realistic "Nonlinear" map, but maps with eta > 3+2*sqrt(2) = 5.828 (Obenchain's "Omega") violate the "Cartesian Monotonicity" Axiom.

Value

Object of class "numeric" of the same length as the tr, ex and cy vectors input:

pref

Computed ICE Economic Preference Scores.

References

Hoch JS, Briggs AH, Willan AR. Something old, something new, something borrowed, something blue: a framework for the marriage of health econometrics and cost-effectiveness analysis. Health Economics 2002; 11: 415-430.

Obenchain RL. ICE Preference Maps: Nonlinear Generalizations of Net Benefit and Acceptability. Health Serv Outcomes Res Method 2008; 8: 31-56. DOI 10.1007/s10742-007-0027-2. Open Access.

Examples

Run this code

# NOT RUN {
  data(sepsis)
  attach(sepsis)
  icedr <- ICEpref(tr=icu, ex=qalypres, cy=totcost, lambda=50000, beta=0.6) # Diminishing Returns.
  summary(icedr)
# }