ICEinfer-package {ICEinfer}R Documentation

ICE Statistical Inference and Economic Preference Variation

Description

Functions in the ICE Statistical Inference package make head-to-head comparisons between patients in two treatment cohorts (assumed to be unbiased samples) in two distinct dimensions, cost and effectiveness.

Bootstrap resampling methods quantify the endogenous Distribution of ICE Uncertainty and define Wedge-Shaped Statistical Confidence Regions equivariant relative to exogenous choice for the numerical Shadow Price of Health, lambda.

Preference maps with (linear or nonlinear) indiference curves can be viewed or superimposed upon endogenous confidence wedges to illustrate that considerable additional, potentially self-contradictory Economic Preference Uncertainty results from deliberately varying lambda.

Details

Package: ICEinfer
Type: Package
Version: 0.2
Date: 2009-02-10
License: GNU GENERAL PUBLIC LICENSE, Version 2, June 1991

Statistical inference using functions from the ICEinfer package usually start with (possibly multiple) invocations of ICEscale() to help determine a reasonable value for the Shadow Price of Health, lambda. This is invariably followed by a single call to ICEuncrt to generate the Bootstrap Distribution of ICE Uncertainty corresponding to the chosen value of lambda. However, the print() and plot() functions for objects of type ICEuncrt do have optional arguments, lfact and swu, to help the user quantify and visualize the consequences of changing lambda and switching between cost and effe units.

Next, a single call to ICEwedge() yields the equivariant, wedge-shaped region of specified statistical confidence within [.50, .99] ...by computing ICE Angle Order Statistics around a circle centered at the ICE Origin, (DeltaEffe, DeltaCost) = (0, 0).

Researchers wishing to view alternative ICE Acceptability Curves would then envoke ICEalice().

Finally, multiple calls to ICEcolor for different values of lambda and/or different forms of (linear or nonlinear) ICE Preference Maps are typically used to illustrate the considerable additional Economic Preference Uncertainty that can be introduced. This Economic Uncertainty is superimposed on top of the inherent Statistical Uncertainty contained in unbiased, patient level data on the relative cost and effectiveness of two treatments for the same disease / condition.

Author(s)

Bob Obenchain <wizbob@att.net>

References

Black WC. The CE plane: a graphic representation of cost-effectiveness. Med Decis Making 1990; 10: 212-214.

Laupacis A, Feeny D, Detsky AS, Tugwell PX. How attractive does a new technology have to be to warrant adoption and utilization? Tentative guidelines for using clinical and economic evaluations. Can Med Assoc J 1992; 146(4): 473-81.

Stinnett AA, Mullahy J. Net health benefits: a new framework for the analysis of uncertainty in cost-effectiveness analysis. Medical Decision Making, Special Issue on Pharmacoeconomics 1998; 18: S68-S80.

O'Brien B, Gersten K, Willan A, Faulkner L. Is there a kink in consumers' threshold value for cost-effectiveness in health care? Health Econ 2002; 11: 175-180.

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.

Obenchain RL. ICEinR.pdf ../R_HOME/library/ICEinfer 2009; 30 pages.

Examples

  demo(dulxparx)

[Package ICEinfer version 0.2-0 Index]