| Title: | Adaptive Design in Clinical Trials |
|---|---|
| Description: | Existing adaptive design methods in clinical trials. The package includes power, stopping boundaries (sample size) calculation functions for two-group group sequential designs, adaptive design with coprimary endpoints, biomarker-informed adaptive design, etc. |
| Authors: | Yalin Zhu |
| Maintainer: | Yalin Zhu <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.0 |
| Built: | 2024-11-16 04:50:41 UTC |
| Source: | https://github.com/allenzhuaz/adct |
Given the Biomarker-Informed design information, returns the overall power and probability of the arm is selected as the winner.
BioInfo.Power(uCtl, u0y, u0x, rhou, suy, sux, rho, sy, sx, Zalpha, N1, N, nArms, nSims)BioInfo.Power(uCtl, u0y, u0x, rhou, suy, sux, rho, sy, sx, Zalpha, N1, N, nArms, nSims)
uCtl |
mean value for the control group. |
u0y |
mean parameter of the group 1 for the parent model. |
u0x |
mean parameter of the group 2 for the parent model. |
rhou |
correlation coefficient between two groups for the parent model. |
suy |
standard deviation of the group 1 for the parent model. |
sux |
standard deviation of the group 2 for the parent model. |
rho |
correlation coefficient between two groups for the lower level model. |
sy |
standard deviation of the group 1 for the lower level model. |
sx |
standard deviation of the group 2 for the lower level model. |
Zalpha |
crtical point for rejection. |
N1 |
sample size per group at interim analysis. |
N |
sample size per group at final analysis. |
nArms |
number of active groups. |
nSims |
number of simulation times. |
The evaluated power and probability of selecting the arm as the winner.
Yalin Zhu
Chang, M. (2014). Adaptive design theory and implementation using SAS and R. CRC Press.
## Determine critical value Zalpha for alpha (power) =0.025 u0y=c(0,0,0); u0x=c(0,0,0) BioInfo.Power(uCtl=0, u0y, u0x, rhou=1, suy=0, sux=0, rho=1, sy=4, sx=4, Zalpha=2.772, N1=100, N=300, nArms=3, nSims=1000) ## Power simulation u0y=c(1,0.5,0.2) u0x=c(2,1,0.5) BioInfo.Power(uCtl=0, u0y, u0x, rhou=0.2, suy=0.2, sux=0.2, rho=0.2, sy=4, sx=4, Zalpha=2.772, N1=100, N=300, nArms=3, nSims=500)## Determine critical value Zalpha for alpha (power) =0.025 u0y=c(0,0,0); u0x=c(0,0,0) BioInfo.Power(uCtl=0, u0y, u0x, rhou=1, suy=0, sux=0, rho=1, sy=4, sx=4, Zalpha=2.772, N1=100, N=300, nArms=3, nSims=1000) ## Power simulation u0y=c(1,0.5,0.2) u0x=c(2,1,0.5) BioInfo.Power(uCtl=0, u0y, u0x, rhou=0.2, suy=0.2, sux=0.2, rho=0.2, sy=4, sx=4, Zalpha=2.772, N1=100, N=300, nArms=3, nSims=500)
Given the group sequential design information, returns the overall power.
CopriEndpt.Power(n, tau, mu1, mu2, rho, alpha1, alpha2, alternative)CopriEndpt.Power(n, tau, mu1, mu2, rho, alpha1, alpha2, alternative)
n |
sample size for the design. |
tau |
information time for the interim analysis. |
mu1 |
mean value for coprimary endpoint 1. |
mu2 |
mean value for coprimary endpoint 2. |
rho |
correlation coefficient between two coprimary endpoints. |
alpha1 |
significant level for the first stage. |
alpha2 |
significant level for the second stage. |
alternative |
indicates the alternative hypothesis and must be one of |
The evaluated power with attributes and computational error.
Yalin Zhu
Chang, M. (2014). Adaptive design theory and implementation using SAS and R. CRC Press.
# Example in Chang (2014) page 272 CopriEndpt.Power(n=197, tau=0.5, mu1=0.2, mu2=0.2, rho=0.5, alpha1=0.0025, alpha2=0.024, alternative="one.sided") sapply(c(-0.8,-0.5,-0.2,0,0.2,0.5,0.8),CopriEndpt.Power, n=197, tau=0.5, mu1=0.2, mu2=0.2, alpha1=0.0025, alpha2=0.024, alternative="one.sided")# Example in Chang (2014) page 272 CopriEndpt.Power(n=197, tau=0.5, mu1=0.2, mu2=0.2, rho=0.5, alpha1=0.0025, alpha2=0.024, alternative="one.sided") sapply(c(-0.8,-0.5,-0.2,0,0.2,0.5,0.8),CopriEndpt.Power, n=197, tau=0.5, mu1=0.2, mu2=0.2, alpha1=0.0025, alpha2=0.024, alternative="one.sided")
Given the group sequential design information, returns the conditional power.
OneArm.CondPower(mu1, mu2, n1, n2, rho, tau, alpha2, alternative)OneArm.CondPower(mu1, mu2, n1, n2, rho, tau, alpha2, alternative)
mu1 |
mean value for the first stage (endpoint 1). |
mu2 |
mean value for the second stage (endpoint 2). |
n1 |
sample size for the first stage. |
n2 |
sample size for the second stage. |
rho |
correlation coefficient between two coprimary endpoints. |
tau |
information time for the interim analysis. |
alpha2 |
significant level for the second stage. |
alternative |
indicates the alternative hypothesis and must be one of |
The evaluated power with attributes and computational error.
Yalin Zhu
Chang, M. (2014). Adaptive design theory and implementation using SAS and R. CRC Press.
# Example in Chang (2014) page 277 OneArm.CondPower(mu1=0.1333, mu2=0.1605, n1=130, n2=130, rho=0.35, tau=0.5, alpha2=0.024, alternative = "one.sided") OneArm.CondPower(mu1=0.1333, mu2=0.1605, n1=130, n2=414, rho=0.35, tau=0.5, alpha2=0.024, alternative = "one.sided")# Example in Chang (2014) page 277 OneArm.CondPower(mu1=0.1333, mu2=0.1605, n1=130, n2=130, rho=0.35, tau=0.5, alpha2=0.024, alternative = "one.sided") OneArm.CondPower(mu1=0.1333, mu2=0.1605, n1=130, n2=414, rho=0.35, tau=0.5, alpha2=0.024, alternative = "one.sided")
Given the group sequential design information, returns the conditional power.
TwoArms.CondPower(mu1, mu2, sigma1, sigma2, n1, n2, rho, tau, alpha2, alternative)TwoArms.CondPower(mu1, mu2, sigma1, sigma2, n1, n2, rho, tau, alpha2, alternative)
mu1 |
mean value for the first stage (endpoint 1). |
mu2 |
mean value for the second stage (endpoint 2). |
sigma1 |
standard deviation for the first stage. |
sigma2 |
standard deviation for the second stage. |
n1 |
sample size for the first stage. |
n2 |
sample size for the second stage. |
rho |
correlation coefficient between two coprimary endpoints. |
tau |
information time for the interim analysis. |
alpha2 |
significant level for the second stage. |
alternative |
indicates the alternative hypothesis and must be one of |
The evaluated power with attributes and computational error.
Yalin Zhu
Chang, M. (2014). Adaptive design theory and implementation using SAS and R. CRC Press.
# Example in Chang (2014) page 278 TwoArms.CondPower(mu1=0.28, sigma1=1.9, mu2=0.35, sigma2=2.2, n1=340, n2=340, rho=0.3, tau=0.5, alpha2=0.024, alternative = "one.sided") TwoArms.CondPower(mu1=0.28, sigma1=1.9, mu2=0.35, sigma2=2.2, n1=340, n2=482, rho=0.3, tau=0.5, alpha2=0.024, alternative = "one.sided") TwoArms.CondPower(mu1=0.32, sigma1=2, mu2=0.4, sigma2=1.8, n1=340, n2=340, rho=0.3, tau=0.5, alpha2=0.024, alternative = "one.sided")# Example in Chang (2014) page 278 TwoArms.CondPower(mu1=0.28, sigma1=1.9, mu2=0.35, sigma2=2.2, n1=340, n2=340, rho=0.3, tau=0.5, alpha2=0.024, alternative = "one.sided") TwoArms.CondPower(mu1=0.28, sigma1=1.9, mu2=0.35, sigma2=2.2, n1=340, n2=482, rho=0.3, tau=0.5, alpha2=0.024, alternative = "one.sided") TwoArms.CondPower(mu1=0.32, sigma1=2, mu2=0.4, sigma2=1.8, n1=340, n2=340, rho=0.3, tau=0.5, alpha2=0.024, alternative = "one.sided")
Given the group sequential design information, returns the simulated overall power.
TwoGrpCopriEndpt.SimPower(mu11,mu12, mu21, mu22, rho, tau, alpha1, alpha2, alternative , Nmax, B)TwoGrpCopriEndpt.SimPower(mu11,mu12, mu21, mu22, rho, tau, alpha1, alpha2, alternative , Nmax, B)
mu11 |
standardized mean value for coprimary endpoint 1 in group 1. |
mu12 |
standardized mean value for coprimary endpoint 2 in group 1. |
mu21 |
standardized mean value for coprimary endpoint 1 in group 2. |
mu22 |
standardized mean value for coprimary endpoint 2 in group 2. |
rho |
correlation coefficient between two coprimary endpoints. |
tau |
information time for the interim analysis. |
alpha1 |
significant level for the first stage. |
alpha2 |
significant level for the second stage. |
alternative |
indicates the alternative hypothesis and must be one of |
Nmax |
maximum sample size per group. |
B |
the simulation iterative time. |
The evaluated power with attributes and computational error.
Yalin Zhu
Chang, M. (2014). Adaptive design theory and implementation using SAS and R. CRC Press.
# Example in Chang (2014) page 275 TwoGrpCopriEndpt.SimPower(mu11=0.2,mu12=0.25, mu21=0.005, mu22=0.015, rho=0.25, tau=0.5, alpha1=0.0025, alpha2=0.024, alternative = "two.sided",Nmax=584, B=10000)# Example in Chang (2014) page 275 TwoGrpCopriEndpt.SimPower(mu11=0.2,mu12=0.25, mu21=0.005, mu22=0.015, rho=0.25, tau=0.5, alpha1=0.0025, alpha2=0.024, alternative = "two.sided",Nmax=584, B=10000)