| Type: | Package |
| Title: | Generalized Two-Stage Designs for Phase II Single-Arm Studies |
| Version: | 1.0 |
| Depends: | R (≥ 2.0.0), graphics, stats, clinfun |
| Author: | Seongho Kim |
| Maintainer: | Seongho Kim <biostatistician.kim@gmail.com> |
| Description: | One can find single-stage and two-stage designs for a phase II single-arm study with either efficacy or safety/toxicity endpoints as described in Kim and Wong (2019) <doi:10.29220/CSAM.2019.26.2.163>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| LazyData: | true |
| NeedsCompilation: | no |
| Packaged: | 2019-05-31 14:38:22 UTC; kimse |
| Repository: | CRAN |
| Date/Publication: | 2019-06-03 12:50:22 UTC |
Generalized Two-Stage Designs for Phase II Single-Arm Studies
Description
The R package gen2stage can generate single-stage and two-stage designs
for phase II single-arm efficacy or safety studies.
Details
| Package: | gen2stage |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2017-10-05 |
| License: | GPL-2 |
Author(s)
Seongho Kim <biostatistician.kim@gmail.com>
References
Kim S and Wong WK. Phase II Two-Stage Single-Arm Clinical Trials for Testing Toxicity Levels. Commun Stat Appl Methods. 2019 Mar;26(2):163-173. https://www.ncbi.nlm.nih.gov/pubmed/31106162.
Examples
# Single-stage safety design with pu (p0) = 0.33 vs. pa (p1) = 0.20
gen2single(0.33, 0.20, 0.05, 0.20)
# Single-stage efficacy design with pu (p0) = 0.67 vs. pa (p1) = 0.80
gen2single(0.67, 0.80, 0.05, 0.20)
# save and print
safety1 = gen2single(0.33, 0.20, 0.05, 0.20)
print(safety1)
# Two-stage safety design with pu (p0) = 0.33 vs. pa (p1) = 0.20
gen2simon(0.33, 0.20, 0.05, 0.20)
gen2simon(0.33, 0.20, 0.05, 0.10, nmax=150)
# Two-stage efficacy design with pu (p0) = 0.67 vs. pa (p1) = 0.80
gen2simon(0.67, 0.80, 0.05, 0.20)
gen2simon(0.67, 0.80, 0.05, 0.10, nmax=150)
# save, print and plot
safety2 = gen2simon(0.33, 0.20, 0.05, 0.20)
print(safety2)
plot(safety2)
Generalized Simon's 2-stage phase II design
Description
Calculates generalized optimal and minimax 2-stage phase II designs based on the R function ph2simon.
Usage
gen2simon(pu, pa, ep1, ep2, nmax=100)
## S3 method for class 'gen2simon'
print(x, ...)
## S3 method for class 'gen2simon'
plot(x, ...)
Arguments
pu |
unacceptable response/toxicity rate |
pa |
response/toxicity rate that is desirable |
ep1 |
threshold for the probability of declaring drug/treatment desirable under p0 |
ep2 |
threshold for the probability of rejecting the drug/treatment under p1 |
nmax |
maximum total sample size (default 100; can be at most 500) |
x |
object returned by gen2simon |
... |
arguments to be passed onto plot and print commands called within |
Value
gen2simon returns a list with pu, pa, alpha, beta and nmax as above and:
out |
matrix of best 2 stage designs for each value of total sample size n. The 6 columns are: r1, n1, r, n, EN(p0), PET(p0), alpha, beta |
The "print" method formats and returns the minimax and optimal designs. The "plot" plots the expected sample size agains the maximum sample size as in Jung et al., 2001
References
Kim S and Wong WK. Phase II Two-Stage Single-Arm Clinical Trials for Testing Toxicity Levels. Commun Stat Appl Methods. 2019 Mar;26(2):163-173. https://www.ncbi.nlm.nih.gov/pubmed/31106162.
Jung SH, Carey M and Kim KM. (2001). Graphical Search for Two-Stage Designs for Phase II Clinical Trials. Controlled Clinical Trials 22, 367-372.
Simon R. (1989). Optimal Two-Stage Designs for Phase II Clinical Trials. Controlled Clinical Trials 10, 1-10.
See Also
Examples
# Two-stage safety design with pu (p0) = 0.33 vs. pa (p1) = 0.20
gen2simon(0.33, 0.20, 0.05, 0.20)
gen2simon(0.33, 0.20, 0.05, 0.10, nmax=150)
# Two-stage efficacy design with pu (p0) = 0.67 vs. pa (p1) = 0.80
gen2simon(0.67, 0.80, 0.05, 0.20)
gen2simon(0.67, 0.80, 0.05, 0.10, nmax=150)
# save, print and plot
safety2 = gen2simon(0.33, 0.20, 0.05, 0.20)
print(safety2)
plot(safety2)
Generalized exact single stage phase II design
Description
Calculates the generalized exact one stage phase II design based on the R function ph2single.
Usage
gen2single(pu,pa,ep1,ep2,nsoln=5)
## S3 method for class 'gen2single'
print(x, ...)
Arguments
pu |
unacceptable response/toxicity rate |
pa |
response/toxicity rate that is desirable |
ep1 |
threshold for the probability of declaring drug/treatment desirable under p0 |
ep2 |
threshold for the probability of rejecting the drug/treatment under p1 |
nsoln |
number of designs with given alpha and beta |
x |
object returned by gen2single |
... |
arguments to be passed onto print command called within |
Value
gen2single returns the optimal design with pu, pa, alpha, and beta as above and:
out |
matrix of the single-stage designs up to nsoln. The 4 columns are: r, n, alpha (type I error), beta (type II erro) |
The "print" method formats and returns the optimal design.
References
Kim S and Wong WK. Phase II Two-Stage Single-Arm Clinical Trials for Testing Toxicity Levels. Commun Stat Appl Methods. 2019 Mar;26(2):163-173. https://www.ncbi.nlm.nih.gov/pubmed/31106162.
See Also
Examples
# Single-stage safety design with pu (p0) = 0.33 vs. pa (p1) = 0.20
gen2single(0.33, 0.20, 0.05, 0.20)
# Single-stage efficacy design with pu (p0) = 0.67 vs. pa (p1) = 0.80
gen2single(0.67, 0.80, 0.05, 0.20)
# save and print
safety1 = gen2single(0.33, 0.20, 0.05, 0.20)
print(safety1)
Two-stage boundary operating characteristics
Description
Calculates the operating characteristics of a two-stage boundary based on the R function oc.twostage.bdry.
Usage
oc.gentwostage.bdry(pu, pa, r1, n1, r, n)
Arguments
pu |
unacceptable response rate |
pa |
response rate that is desirable |
r1 |
first stage threshold to declare treatment undesirable |
n1 |
first stage sample size |
r |
overall threshold to declare treatment undesirable |
n |
total sample size |
Value
oc.gentwostage.bdry returns the type I and II error rates as well as the probability of early temination and expected sample size under pu for a specific boundary.
References
Kim S and Wong WK. Phase II Two-Stage Single-Arm Clinical Trials for Testing Toxicity Levels. Commun Stat Appl Methods. 2019 Mar;26(2):163-173. https://www.ncbi.nlm.nih.gov/pubmed/31106162.
See Also
Examples
# Optimal two-stage safety design with pu (p0) = 0.33 vs. pa (p1) = 0.20
oc.gentwostage.bdry(0.33, 0.20, 8, 26, 22, 85)
# Optimal two-stage efficacy design with pu (p0) = 0.67 vs. pa (p1) = 0.80
oc.gentwostage.bdry(0.67, 0.80, 18, 26, 63, 85)