Incremental Cost-Effectiveness (ICE) Statistical Inference (from Two Unbiased Samples)
Given two unbiased samples of patient level data on cost and effectiveness
for a pair of treatments, make head-to-head treatment comparisons by (i) generating the
bivariate bootstrap resampling distribution of ICE uncertainty for a specified value of
the shadow price of health, lambda, (ii) form the wedge-shaped ICE confidence region with
specified confidence fraction within [0.50, 0.99] that is equivariant with respect to
changes in lambda, (iii) color the bootstrap outcomes within the above confidence wedge
with economic preferences from an ICE map with specified values of lambda, beta and gamma
parameters, (iv) display VAGR and ALICE acceptability curves, and (v) display indifference
(iso-preference) curves from an ICE map with specified values of lambda, beta and gamma or
eta parameters.
GPL version 2 or later; softrx@iquest.net

Incremental Cost-Effectiveness (ICE) Statistical Inference from Two Unbiased Samples

Incremental Cost-Effectiveness (ICE) Statistical Inference from
Two Unbiased Samples
Given two unbiased samples of patient level data on cost
and effectiveness for a pair of treatments, make head-to-head
treatment comparisons by (i) generating the bivariate bootstrap
resampling distribution of ICE uncertainty for a specified
value of the shadow price of health, lambda, (ii) form the
wedge-shaped ICE confidence region with specified confidence
fraction within [0.50, 0.99] that is equivariant with respect
to changes in lambda, (iii) color the bootstrap outcomes within
the above confidence wedge with economic preferences from an
ICE map with specified values of lambda, beta and gamma
parameters, (iv) display VAGR and ALICE acceptability curves,
and (v) illustrate variation in ICE preferences by displaying
potentially non-linear indifference(iso-preference) curves from
an ICE map with specified values of lambda, beta and gamma or
eta parameters.

Given two unbiased samples of patient level data on cost and effectiveness
for a pair of treatments, make head-to-head treatment comparisons by (i) generating the
bivariate bootstrap resampling distribution of ICE uncertainty for a specified value of
the shadow price of health, lambda, (ii) form the wedge-shaped ICE confidence region with
specified confidence fraction within [0.50, 0.99] that is equivariant with respect to
changes in lambda, (iii) color the bootstrap outcomes within the above confidence wedge
with economic preferences from an ICE map with specified values of lambda, beta and gamma
parameters, (iv) display VAGR and ALICE acceptability curves, and (v) illustrate variation
in ICE preferences by displaying potentially non-linear indifference(iso-preference) curves
from an ICE map with specified values of lambda, beta and gamma or eta parameters.