| 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: | 1.3 |
| Date: | 2020-10-10 |
| License: | GNU GENERAL PUBLIC LICENSE, Version 2, June 1991 |
Statistical inference using functions from the ICEinfer package usually starts 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. The print() and plot() functions for objects of type ICEuncrt have optional arguments, lfact and swu, to help users quantify and visualize the consequences of changing lambda and switching between cost and effe units.
A single call to ICEwedge() then yields the equivariant, wedge-shaped region of specified statistical confidence within [.50, .99] ...by computing ICE Angle Order Statistics around a circle with center 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 in these ways. This Economic Preference uncertainty is superimposed on top of the inherent Statistical Uncertainty contained within even unbiased, patient level data on the relative cost and effectiveness of two treatments for the same disease or health 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.
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.
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.
O'Brien B, Gersten K, Willan A, Faulkner L. Is there a kink in consumers' threshold value for cost-effectiveness in health care? Health Economics 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. (2020) ICEinfer_in_R.PDF ICEinfer package vignette-like document. http://localcontrolstatistics.org
Stinnett AA. Adjusting for Bias in C/E Ratio Estimates. Health Economics LETTERS Secton. 1996; 5: 470-472.
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.
Examples
demo(fluoxpin)