IUT.design {ph2mult} | R Documentation |
The design function for multinomial designs under intersection-union test (IUT)
Description
Search the type I error or power of a multinomial (response and disease progression) single- or two-stage design under IUT:
H_0: p_1 \le p_{01} \ OR \ p_2 \ge p_{02} \ versus \ H_1: p_1 \ge p_{11} > p_{01} \ AND \ p_2 \le p_{12} < p_{02}
Usage
IUT.design(method = c("s1", "s2", "s2.f"),
s1.rej, t1.rej, s1.acc, t1.acc, n1, s2.rej, t2.rej, n2,
s1.rej.delta=0, t1.rej.delta=0, s1.acc.delta=0, t1.acc.delta=0,
s2.rej.delta=0, t2.rej.delta=0, n1.delta=0, n2.delta=0,
p0.s, p0.t, p1.s, p1.t, signif.level = 0.05, power.level = 0.85,
show.time = TRUE, output = c("minimax","optimal","maxpower","admissible", "all"),
plot.out=FALSE)
Arguments
method |
design methods according to number of stage and stopping rule, "s1" represents single-stage design stopping for both efficacy and futility, "s2" represents two-stage design stopping for both efficacy and futility, "s2.f" represents two-stage design stopping for futility only. |
s1.rej |
first stage responses threshold to stop the trial for efficacy. Applied for "s1" or "s2". |
t1.rej |
first stage disease progressions threshold to stop the trial for efficacy. Applied for "s1" or "s2". |
s1.acc |
first stage responses threshold to stop the trial for futility. Applied for "s2" or "s2.f". |
t1.acc |
first stage disease progressions threshold to stop the trial for futility. Applied for "s2" or "s2.f". |
n1 |
first stage sample size. Applied for "s1", "s2" or "s2.f". |
s2.rej |
second stage responses threshold to stop the trial for efficacy. Applied for "s2" or "s2.f". |
t2.rej |
second stage disease progressions threshold to stop the trial for efficacy. Applied for "s2" or "s2.f". |
n2 |
second stage sample size. Applied for "s2" or "s2.f". |
s1.rej.delta |
pre-specified search difference for s1.rej. |
t1.rej.delta |
pre-specified search difference for t1.rej. |
s1.acc.delta |
pre-specified search difference for s1.acc. |
t1.acc.delta |
pre-specified search difference for t1.acc. |
s2.rej.delta |
pre-specified search difference for s2.rej. |
t2.rej.delta |
pre-specified search difference for t2.rej. |
n1.delta |
pre-specified search difference for n1. |
n2.delta |
pre-specified search difference for n2. |
p0.s |
pre-specified response rate under null hypothesis. |
p0.t |
pre-specified disease progression rate under null hypothesis. |
p1.s |
pre-specified response rate under alternative hypothesis. |
p1.t |
pre-specified disease progression rate under alternative hypothesis.
Note: type I error calculation needs to take maximum of the power function with |
signif.level |
pre-specified significant level. |
power.level |
pre-specified power level. |
show.time |
logical; if TRUE (default), show the calculation time for the search function. |
output |
the output types of design, choose from "minimax","optimal","admissible" and "maxpower". |
plot.out |
logical; if TRUE, output a plot for design selection. |
Value
boundset |
the boundaries set satisfying the design types properties: |
References
Chang, M. N., Devidas, M., & Anderson, J. (2007). One- and two-stage designs for phase II window studies. Statistics in medicine , 26(13), 2604-2614.
Simon, R. (1989). Optimal two-stage designs for phase II clinical trials. Controlled clinical trials 10(1), 1-10.
Jung, S. H., Lee, T., Kim, K., & George, S. L. (2004). Admissible two-stage designs for phase II cancer clinical trials. Statistics in medicine 23(4), 561-569.
Examples
p01=0.1; p02=0.9
## Calculate type I error for single-stage design
IUT.design(method="s1",s1.rej=18, t1.rej = 12, n1=80,
s1.rej.delta = 1, t1.rej.delta = 1, n1.delta=1,
p0.s = 0.15, p0.t = 0.25, p1.s = 0.3, p1.t= 0.1, output = "minimax")
## Designs for two-stage design, output PET and EN under null hypothesis
IUT.design(method="s2",s1.rej = 11, t1.rej = 4, s1.acc=8, t1.acc = 5, n1=40,
s2.rej=18, t2.rej = 11, n2=40, p0.s = 0.15, p0.t = 0.25, p1.s = 0.3, p1.t= 0.1, output = "minimax")
IUT.design(method="s2",s1.rej = 11, t1.rej = 4, s1.acc=8, t1.acc = 5, n1=40,
s2.rej=18, t2.rej = 11, n2=40, p0.s = 0.15, p0.t = 0.25, p1.s = 0.3, p1.t= 0.1, output = "optimal")