Type: Package
Title: Using Multiple Continuous Biomarkers for Patient Enrichment in Two-Stage Clinical Designs
Version: 1.0.1
Description: Enrichment strategies play a critical role in modern clinical trial design, especially as precision medicine advances the focus on patient-specific efficacy. Recent developments in enrichment design have introduced biomarker randomness and accounted for the correlation structure between treatment effect and biomarker, resulting in a two-stage threshold enrichment design. We propose novel two-stage enrichment designs capable of handling two or more continuous biomarkers. See Zhang, F. and Gou, J. (2025). Using multiple biomarkers for patient enrichment in two-stage clinical designs. Technical Report.
License: GPL-3
Encoding: UTF-8
Depends: R (≥ 4.2.0)
Imports: tmvtnorm (≥ 1.2), stats (≥ 4.0.0)
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-04-10 15:25:34 UTC; psystat
Author: Jiangtao Gou [aut, cre], Fengqing (Zoe) Zhang [aut]
Maintainer: Jiangtao Gou <gouRpackage@gmail.com>
Repository: CRAN
Date/Publication: 2025-04-15 19:30:12 UTC

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)

Find the cutoff values of biomarkers based on the average subpopulation treatment effect

Description

Find the cutoff values of biomarkers based on the average subpopulation treatment effect

Usage

findATE2(z2interval, kkk, muminusmu0, kappa, rhovec, sigma, cDel)

Arguments

z2interval

a numeric vector of two values, including the lower and upper limits of the initial interval for z2

kkk

the researchers' weighting preference between the two biomarkers

muminusmu0

a number of the difference between the mean of outcome and the minimal clinically important treatment effect

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

cDel

the desired average subpopulation treatment effect

Value

a numeric vector of two values which are the cutoff values for z1 and z2

Author(s)

Jiangtao Gou

Fengqing Zhang

References

Zhang, F. and Gou, J. (2025). Using multiple biomarkers for patient enrichment in two-stage clinical designs. Technical Report.

Examples

z2interval <- c(-5, 5)
kkk <- 1
muminusmu0 <- 1.8
kappa <- 0.1
rhovec <- c(0.1, 0.2)
sigma <- 1
cDel <- 2.5
findATE2(z2interval, kkk, muminusmu0, kappa, rhovec, sigma, cDel)

Find the cutoff values of biomarkers based on the standardized average subpopulation treatment effect

Description

Find the cutoff values of biomarkers based on the standardized average subpopulation treatment effect

Usage

findSATE2(z2interval, kkk, muminusmu0, kappa, rhovec, sigma, cLam)

Arguments

z2interval

a numeric vector of two values, including the lower and upper limits of the initial interval for z2

kkk

the researchers' weighting preference between the two biomarkers

muminusmu0

a number of the difference between the mean of outcome and the minimal clinically important treatment effect

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

cLam

the desired standardized average subpopulation treatment effect

Value

a numeric vector of two values which are the cutoff values for z1 and z2

Author(s)

Jiangtao Gou

Fengqing Zhang

References

Zhang, F. and Gou, J. (2025). Using multiple biomarkers for patient enrichment in two-stage clinical designs. Technical Report.

Examples

z2interval <- c(-4, 4)
kkk <- 1
muminusmu0 <- 1.8
kappa <- 0.1
rhovec <- c(0.1, 0.2)
sigma <- 1
cLam <- 2.5
findSATE2(z2interval, kkk, muminusmu0, kappa, rhovec, sigma, cLam)

Find the difference between the average subpopulation treatment effect and the desired one

Description

Find the difference between the average subpopulation treatment effect and the desired one

Usage

targetDel(z2, kkk, muminusmu0, kappa, rhovec, sigma, cDel)

Arguments

z2

the standardized biomarker value of the second biomarker

kkk

the researchers' weighting preference between the two biomarkers

muminusmu0

a number of the difference between the mean of outcome and the minimal clinically important treatment effect

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

cDel

the desired average subpopulation treatment effect

Value

the difference between the average subpopulation treatment effect and the desired one


Find the difference between the standardized average subpopulation treatment effect and the desired one

Description

Find the difference between the standardized average subpopulation treatment effect and the desired one

Usage

targetLam(z2, kkk, muminusmu0, kappa, rhovec, sigma, cLam)

Arguments

z2

the standardized biomarker value of the second biomarker

kkk

the researchers' weighting preference between the two biomarkers

muminusmu0

a number of the difference between the mean of outcome and the minimal clinically important treatment effect

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

cLam

the desired standardized average subpopulation treatment effect

Value

the difference between the standardized average subpopulation treatment effect and the desired one