avetrteff2 {cobenrich} | R Documentation |
Compute the average subpopulation treatment effect and the standardized average subpopulation treatment effect when two biomarkers are involved
Description
Compute the average subpopulation treatment effect and the standardized average subpopulation treatment effect when two biomarkers are involved
Usage
avetrteff2(z1z2, kappa, rhovec, sigma, muminusmu0)
Arguments
z1z2 |
a numeric vector of two numbers that are standardized biomarker values |
kappa |
a number of the correlation coefficient between two biomarkers |
rhovec |
a numeric vector of two correlation coefficients between the output and two biomarkers |
sigma |
a number of the standard deviation of outcome |
muminusmu0 |
a number of the difference between the mean of outcome and the minimal clinically important treatment effect |
Value
a list of three numbers: delta
is the average subpopulation treatment effect, lambda
is the standardized average subpopulation treatment effect, and cVar
is the variance with respect to the truncated distribution with specified cutoff values
Author(s)
Jiangtao Gou
References
Zhang, F. and Gou, J. (2025). Using multiple biomarkers for patient enrichment in two-stage clinical designs. Technical Report.
Examples
x1x2 <- c(2, 1)
nu1nu2 <- c(0,0)
tau1tau2 <- c(1,1)
z1z2 <- (x1x2 - nu1nu2)/tau1tau2
muminusmu0 <- 1.8
kappa <- 0.1
sigma <- 1
rhovec <- c(0.1, 0.2)
avetrteff2(z1z2, kappa, rhovec, sigma, muminusmu0)