calculateIAE {braidReports} | R Documentation |
Calculate the Index of Achievable Efficacy
Description
Calculates the index of achievable efficacy, or IAE, (a measure of a drug combination's efficacy relative to toxicological and pharmacological constraints) based on a response surface representation of a combination in the form of a full BRAID surface parameter vector.
Usage
calculateIAE(parv, lev, macs, same = FALSE)
Arguments
parv |
a full 10-element BRAID surface parameter vector |
lev |
a single value or vector representing the effect level or levels at which the IAE should be calculated |
macs |
the maximum achievable concentrations of the two drugs whose effects are represented by the response surface. A single value may be given, which will be used for both drugs |
same |
a boolean variable representing whether the two drugs in the combination are
believed to be the same drug (their dose-response parameters need not be identical). If
|
Details
The IAE is a measure of the aggregate ratio of achievable dose-pairs for a given combination and the minimum dose pairs required to achieve a given effect. Formally, it is defined as
IAE=\left(\frac{\int_{AC}{dD_A dD_B}}{\int_{AC}{\left(1-H\left(\frac{E_{AB}(D_A,D_B)-E}{E_f-E}\right)\right)dD_A dD_B}}\right)^{1/2}
where AC
is the space of all achievable dose pairs as determined by pharmacological or
toxicological constraints. In this function, this space is represented either as a rectangular
region in dose-pair-space bounded by the two values in the parameter macs
(when same
is FALSE
) or the lower triangular region of dose-pair-space in which the sum of the two
doses is less than the single value in macs
(when same
is TRUE
).
Value
A single value or vector of values containing the estimated IAE for the response surface at the
effect level or levels specified in lev
.
Author(s)
Nathaniel R. Twarog
References
Twarog, N.R., Stewart, E., Vowell Hamill, C., and Shelat, A. BRAID: A Unifying Paradigm for the Analysis of Combined Drug Action. Scientific Reports In Press (2016).
See Also
Examples
data(es8analysis)
bfit <- es8analysis$braidFit
# Modelled effect is base-10 logarithm of cell survival relative to negative controls,
# so a level of -2 reflects 99% cell killing
calculateIAE(bfit$fullpar,c(-1,-2),c(1.015*10^-6,5*10^-5))