| Title: | Bayesian Decreasingly Informative Priors for Early Termination Phase II Trials |
|---|---|
| Description: | Provide early termination phase II trial designs with a decreasingly informative prior (DIP) or a regular Bayesian prior chosen by the user. The program can determine the minimum planned sample size necessary to achieve the user-specified admissible designs. The program can also perform power and expected sample size calculations for the tests in early termination Phase II trials. See Wang C and Sabo RT (2022) <doi:10.18203/2349-3259.ijct20221110>; Sabo RT (2014) <doi:10.1080/10543406.2014.888441>. |
| Authors: | Chen Wang [cre, aut], Roy Sabo [aut] |
| Maintainer: | Chen Wang <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.1 |
| Built: | 2025-02-16 04:00:47 UTC |
| Source: | https://github.com/chenw10/bayesdip |
For a given planned sample size, the efficacy and futility boundaries, return the power, the type I error, the expected sample size and its standard deviation, the probability of reaching the efficacy and futility boundaries.
OneSampleBernoulli( prior, N = 100, p0, p1, d = 0, ps = 0.95, pf = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 5000 )OneSampleBernoulli( prior, N = 100, p0, p1, d = 0, ps = 0.95, pf = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 5000 )
prior |
A list of length 3 containing the distributional information of the prior. The first element is a number specifying the type of prior. Options are
The second and third elements of the list are the parameters a and b, respectively. |
N |
The planned sample size. |
p0 |
The null response rate, which could be taken as the standard or historical rate. |
p1 |
The response rate of the new treatment. |
d |
The target improvement (minimal clinically meaningful difference). |
ps |
The efficacy boundary (upper boundary). |
pf |
The futility boundary (lower boundary). |
alternative |
less (lower values imply greater efficacy) or greater (larger values imply greater efficacy). |
seed |
The seed for simulations. |
sim |
The number of simulations. |
A list of the arguments with method and computed elements
# with traditional Bayesian prior Beta(1,1) OneSampleBernoulli(list(2,1,1), N = 100, p0 = 0.3, p1 = 0.5, d = 0.05, ps = 0.98, pf = 0.05, alternative = "greater", seed = 202210, sim = 10) # with DIP OneSampleBernoulli(list(1,0,0), N = 100, p0 = 0.3, p1 = 0.5, d = 0.05, ps = 0.98, pf = 0.05, alternative = "greater", seed = 202210, sim = 10)# with traditional Bayesian prior Beta(1,1) OneSampleBernoulli(list(2,1,1), N = 100, p0 = 0.3, p1 = 0.5, d = 0.05, ps = 0.98, pf = 0.05, alternative = "greater", seed = 202210, sim = 10) # with DIP OneSampleBernoulli(list(1,0,0), N = 100, p0 = 0.3, p1 = 0.5, d = 0.05, ps = 0.98, pf = 0.05, alternative = "greater", seed = 202210, sim = 10)
Calculate the minimum planned sample size under an admissible design. The users decide the power and type-I-error, and pick the efficacy and futility boundaries. If there are no admissible design based on controlled type-I-error, then default to output the designs with the lowest type-I-error and at least the user-defined (e.g. 80%) power.
OneSampleBernoulli.Design( prior, nmin = 10, nmax = 100, p0, p1, d = 0, ps, pf, power = 0.8, t1error = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 1000 )OneSampleBernoulli.Design( prior, nmin = 10, nmax = 100, p0, p1, d = 0, ps, pf, power = 0.8, t1error = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 1000 )
prior |
A list of length 3 containing the distributional information of the prior. The first element is a number specifying the type of prior. Options are
The second and third elements of the list are the parameters a and b, respectively. |
nmin |
The start searching sample size |
nmax |
The stop searching sample size |
p0 |
The null response rate, which could be taken as the standard or historical rate. |
p1 |
The response rate of the new treatment. |
d |
The target improvement (minimal clinically meaningful difference). |
ps |
The efficacy boundary (upper boundary). |
pf |
The futility boundary (lower boundary). |
power |
The power to achieve. |
t1error |
The controlled type-I-error. |
alternative |
less (lower values imply greater efficacy) or greater (larger values imply greater efficacy). |
seed |
The seed for simulations. |
sim |
The number of simulations. |
A list of the arguments with method and computed elements.
# with traditional Bayesian prior Beta(1,1) OneSampleBernoulli.Design(list(2,1,1), nmin = 10, nmax=100, p0 = 0.3, p1 = 0.5, d = 0, ps = 0.98, pf = 0.02, power = 0.80, t1error=0.05, alternative = "greater", seed = 202210, sim = 10) # with DIP OneSampleBernoulli.Design(list(1,0,0), nmin = 10, nmax=100, p0 = 0.3, p1 = 0.5, d = 0, ps = 0.98, pf = 0.02, power = 0.80, t1error=0.05, alternative = "greater", seed = 202210, sim = 10)# with traditional Bayesian prior Beta(1,1) OneSampleBernoulli.Design(list(2,1,1), nmin = 10, nmax=100, p0 = 0.3, p1 = 0.5, d = 0, ps = 0.98, pf = 0.02, power = 0.80, t1error=0.05, alternative = "greater", seed = 202210, sim = 10) # with DIP OneSampleBernoulli.Design(list(1,0,0), nmin = 10, nmax=100, p0 = 0.3, p1 = 0.5, d = 0, ps = 0.98, pf = 0.02, power = 0.80, t1error=0.05, alternative = "greater", seed = 202210, sim = 10)
For a given planned sample size, the efficacy and futility boundaries, return the power, the type I error, the expected sample size and its standard deviation, the probability of reaching the efficacy and futility boundaries.
OneSampleNormal1( prior, N = 100, mu0, mu1, var, d = 0, ps = 0.95, pf = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 5000 )OneSampleNormal1( prior, N = 100, mu0, mu1, var, d = 0, ps = 0.95, pf = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 5000 )
prior |
A list of length 2 containing the distributional information of the prior. The first element is a number specifying the type of prior. Options are
The second elements of the list is the parameter n0. |
N |
The planned sample size. |
mu0 |
The null mean value, which could be taken as the standard or current mean. |
mu1 |
The mean value of the new treatment. |
var |
The variance |
d |
The target improvement (minimal clinically meaningful difference). |
ps |
The efficacy boundary (upper boundary). |
pf |
The futility boundary (lower boundary). |
alternative |
less (lower values imply greater efficacy) or greater (larger values imply greater efficacy). |
seed |
The seed for simulations. |
sim |
The number of simulations. |
A list of the arguments with method and computed elements.
# with traditional Bayesian prior Beta(1,1) OneSampleNormal1(list(2,6), N = 100, mu0 = 100, mu1 = 95, var=15, d = 0.05, ps = 0.95, pf = 0.05, alternative = "less", seed = 202210, sim = 10) OneSampleNormal1(list(1,0), N = 100, mu0 = 100, mu1 = 95, var=15, d = 0.05, ps = 0.95, pf = 0.05, alternative = "less", seed = 202210, sim = 10)# with traditional Bayesian prior Beta(1,1) OneSampleNormal1(list(2,6), N = 100, mu0 = 100, mu1 = 95, var=15, d = 0.05, ps = 0.95, pf = 0.05, alternative = "less", seed = 202210, sim = 10) OneSampleNormal1(list(1,0), N = 100, mu0 = 100, mu1 = 95, var=15, d = 0.05, ps = 0.95, pf = 0.05, alternative = "less", seed = 202210, sim = 10)
#' Calculate the minimum planned sample size under an admissible design. The users decide the power and type-I-error, and pick the efficacy and futility boundaries. If there are no admissible design based on controlled type-I-error, then default to output the designs with the lowest type-I-error and at least the user-defined (e.g. 80%) power.
OneSampleNormal1.Design( prior, nmin = 10, nmax = 100, mu0, mu1, var, d = 0, ps, pf, power = 0.8, t1error = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 1000 )OneSampleNormal1.Design( prior, nmin = 10, nmax = 100, mu0, mu1, var, d = 0, ps, pf, power = 0.8, t1error = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 1000 )
prior |
A list of length 2 containing the distributional information of the prior. The first element is a number specifying the type of prior. Options are
The second elements of the list is the parameter n0. |
nmin |
The start searching sample size |
nmax |
The stop searching sample size |
mu0 |
The null mean value, which could be taken as the standard or current mean. |
mu1 |
The mean value of the new treatment. |
var |
The variance |
d |
The target improvement (minimal clinically meaningful difference). |
ps |
The efficacy boundary (upper boundary). |
pf |
The futility boundary (lower boundary). |
power |
The power to achieve. |
t1error |
The controlled type-I-error. |
alternative |
less (lower values imply greater efficacy) or greater (larger values imply greater efficacy). |
seed |
The seed for simulations. |
sim |
The number of simulations. |
A list of the arguments with method and computed elements.
# with traditional Bayesian prior Beta(1,1) OneSampleNormal1.Design(list(2,6), nmin = 10, nmax = 100, mu0 = 100, mu1 = 95, var=15, d = 0.05, ps = 0.95, pf = 0.05, power = 0.8, t1error = 0.05, alternative = "less", seed = 202210, sim = 10) # with DIP OneSampleNormal1.Design(list(1,0), nmin = 10, nmax = 100, mu0 = 100, mu1 = 95, var=15, d = 0.05, ps = 0.95, pf = 0.05, power = 0.8, t1error = 0.05, alternative = "less", seed = 202210, sim = 10)# with traditional Bayesian prior Beta(1,1) OneSampleNormal1.Design(list(2,6), nmin = 10, nmax = 100, mu0 = 100, mu1 = 95, var=15, d = 0.05, ps = 0.95, pf = 0.05, power = 0.8, t1error = 0.05, alternative = "less", seed = 202210, sim = 10) # with DIP OneSampleNormal1.Design(list(1,0), nmin = 10, nmax = 100, mu0 = 100, mu1 = 95, var=15, d = 0.05, ps = 0.95, pf = 0.05, power = 0.8, t1error = 0.05, alternative = "less", seed = 202210, sim = 10)
For a given planned sample size, the efficacy and futility boundaries, return the power, the type I error, the expected sample size and its standard deviation, the probability of reaching the efficacy and futility boundaries.
OneSampleNormal2( prior, N = 100, mu0, mu1, var0, var, d = 0, ps = 0.95, pf = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 5000 )OneSampleNormal2( prior, N = 100, mu0, mu1, var0, var, d = 0, ps = 0.95, pf = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 5000 )
prior |
A list of length 3 containing the distributional information of the prior. The first element is a number specifying the type of prior. Options are
The second and third elements of the list are the parameters k and v, respectively. |
N |
The planned sample size. |
mu0 |
The null mean value, which could be taken as the standard or current mean. |
mu1 |
The mean value of the new treatment. |
var0 |
The prior sample variance |
var |
The variance |
d |
The target improvement (minimal clinically meaningful difference). |
ps |
The efficacy boundary (upper boundary). |
pf |
The futility boundary (lower boundary). |
alternative |
less (lower values imply greater efficacy) or greater (larger values imply greater efficacy). |
seed |
The seed for simulations. |
sim |
The number of simulations. |
A list of the arguments with method and computed elements.
# with traditional Bayesian prior Beta(1,1) OneSampleNormal2(list(2,2,1), N = 100, mu0 = 100, mu1 = 95, var0=225, var=225, d = 0, ps = 0.95, pf = 0.05, alternative = "less", seed = 202210, sim = 10) # with DIP OneSampleNormal2(list(1,0,0), N = 100, mu0 = 100, mu1 = 95, var0=225, var=225, d = 0, ps = 0.95, pf = 0.05, alternative = "less", seed = 202210, sim = 10)# with traditional Bayesian prior Beta(1,1) OneSampleNormal2(list(2,2,1), N = 100, mu0 = 100, mu1 = 95, var0=225, var=225, d = 0, ps = 0.95, pf = 0.05, alternative = "less", seed = 202210, sim = 10) # with DIP OneSampleNormal2(list(1,0,0), N = 100, mu0 = 100, mu1 = 95, var0=225, var=225, d = 0, ps = 0.95, pf = 0.05, alternative = "less", seed = 202210, sim = 10)
Calculate the minimum planned sample size under an admissible design. The users decide the power and type-I-error, and pick the efficacy and futility boundaries. If there are no admissible design based on controlled type-I-error, then default to output the designs with the lowest type-I-error and at least the user-defined (e.g. 80%) power.
OneSampleNormal2.Design( prior, nmin = 10, nmax = 100, mu0, mu1, var0, var, d = 0, ps, pf, power = 0.8, t1error = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 1000 )OneSampleNormal2.Design( prior, nmin = 10, nmax = 100, mu0, mu1, var0, var, d = 0, ps, pf, power = 0.8, t1error = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 1000 )
prior |
A list of length 3 containing the distributional information of the prior. The first element is a number specifying the type of prior. Options are
The second and third elements of the list are the parameters k and v, respectively. |
nmin |
The start searching sample size |
nmax |
The stop searching sample size |
mu0 |
The null mean value, which could be taken as the standard or current mean. |
mu1 |
The mean value of the new treatment. |
var0 |
The prior sample variance |
var |
The variance |
d |
The target improvement (minimal clinically meaningful difference). |
ps |
The efficacy boundary (upper boundary). |
pf |
The futility boundary (lower boundary). |
power |
The power to achieve. |
t1error |
The controlled type-I-error. |
alternative |
less (lower values imply greater efficacy) or greater (larger values imply greater efficacy). |
seed |
The seed for simulations. |
sim |
The number of simulations. |
A list of the arguments with method and computed elements.
# with traditional Bayesian prior Beta(1,1) OneSampleNormal2.Design(list(2,2,1), nmin = 10, nmax = 100, mu0 = 100, mu1 = 95, var0=225, var=225, d = 0, ps = 0.95, pf = 0.05, power = 0.8, t1error = 0.05, alternative = "less", seed = 202210, sim = 10) # with DIP OneSampleNormal2.Design(list(1,0,0), nmin = 10, nmax = 100, mu0 = 100, mu1 = 95, var0=225, var=225, d = 0, ps = 0.95, pf = 0.05, power = 0.8, t1error = 0.05, alternative = "less", seed = 202210, sim = 10)# with traditional Bayesian prior Beta(1,1) OneSampleNormal2.Design(list(2,2,1), nmin = 10, nmax = 100, mu0 = 100, mu1 = 95, var0=225, var=225, d = 0, ps = 0.95, pf = 0.05, power = 0.8, t1error = 0.05, alternative = "less", seed = 202210, sim = 10) # with DIP OneSampleNormal2.Design(list(1,0,0), nmin = 10, nmax = 100, mu0 = 100, mu1 = 95, var0=225, var=225, d = 0, ps = 0.95, pf = 0.05, power = 0.8, t1error = 0.05, alternative = "less", seed = 202210, sim = 10)
For a given planned sample size, the efficacy and futility boundaries, return the power, the type I error, the expected sample size and its standard deviation, the probability of reaching the efficacy and futility boundaries.
OneSamplePoisson( prior, N = 100, m0, m1, d = 0, ps = 0.95, pf = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 5000 )OneSamplePoisson( prior, N = 100, m0, m1, d = 0, ps = 0.95, pf = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 5000 )
prior |
A list of length 3 containing the distributional information of the prior. The first element is a number specifying the type of prior. Options are
The second and third elements of the list are the parameters a and b, respectively. |
N |
The planned sample size. |
m0 |
The null event rate, which could be taken as the standard or current event rate. |
m1 |
The event rate of the new treatment. |
d |
The target improvement (minimal clinically meaningful difference). |
ps |
The efficacy boundary (upper boundary). |
pf |
The futility boundary (lower boundary). |
alternative |
less (lower values imply greater efficacy) or greater (larger values imply greater efficacy). |
seed |
The seed for simulations. |
sim |
The number of simulations. |
A list of the arguments with method and computed elements
# with traditional Bayesian prior Gamma(0.5,0.001) OneSamplePoisson(list(2,0.5,0.001), N = 100, m0 = 0.5, m1 = 0.4, d = 0.05, ps = 0.95, pf = 0.05, alternative = "less", seed = 202210, sim = 10) # with DIP OneSamplePoisson(list(1,0,0), N = 100, m0 = 0.5, m1 = 0.4, d = 0.05, ps = 0.95, pf = 0.05, alternative = "less", seed = 202210, sim = 10)# with traditional Bayesian prior Gamma(0.5,0.001) OneSamplePoisson(list(2,0.5,0.001), N = 100, m0 = 0.5, m1 = 0.4, d = 0.05, ps = 0.95, pf = 0.05, alternative = "less", seed = 202210, sim = 10) # with DIP OneSamplePoisson(list(1,0,0), N = 100, m0 = 0.5, m1 = 0.4, d = 0.05, ps = 0.95, pf = 0.05, alternative = "less", seed = 202210, sim = 10)
Calculate the minimum planned sample size under an admissible design. The users decide the power and type-I-error, and pick the efficacy and futility boundaries. If there are no admissible design based on controlled type-I-error, then default to output the designs with the lowest type-I-error and at least the user-defined (e.g. 80%) power.
OneSamplePoisson.Design( prior, nmin = 10, nmax = 100, m0, m1, d = 0, ps, pf, power = 0.8, t1error = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 1000 )OneSamplePoisson.Design( prior, nmin = 10, nmax = 100, m0, m1, d = 0, ps, pf, power = 0.8, t1error = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 1000 )
prior |
A list of length 3 containing the distributional information of the prior. The first element is a number specifying the type of prior. Options are
The second and third elements of the list are the parameters a and b, respectively. |
nmin |
The start searching sample size |
nmax |
The stop searching sample size |
m0 |
The null event rate, which could be taken as the standard or current event rate. |
m1 |
The event rate of the new treatment. |
d |
The target improvement (minimal clinically meaningful difference). |
ps |
The efficacy boundary (upper boundary). |
pf |
The futility boundary (lower boundary). |
power |
The expected power to achieve. |
t1error |
The controlled type-I-error. |
alternative |
less (lower values imply greater efficacy) or greater (larger values imply greater efficacy). |
seed |
The seed for simulations. |
sim |
The number of simulations. |
A list of the arguments with method and computed elements
# with traditional Bayesian prior Gamma(0.5,0.001) OneSamplePoisson.Design(list(2,0.5,0.001), nmin = 10, nmax=100, m0 = 5, m1 = 4, d = 0, ps = 0.95, pf = 0.05, power = 0.80, t1error=0.05, alternative = "less", seed = 202210, sim = 10) # with DIP OneSamplePoisson.Design(list(1,0,0), nmin = 10, nmax=100, m0 = 5, m1 = 4, d = 0, ps = 0.95, pf = 0.05, power = 0.80, t1error=0.05, alternative = "less", seed = 202210, sim = 10)# with traditional Bayesian prior Gamma(0.5,0.001) OneSamplePoisson.Design(list(2,0.5,0.001), nmin = 10, nmax=100, m0 = 5, m1 = 4, d = 0, ps = 0.95, pf = 0.05, power = 0.80, t1error=0.05, alternative = "less", seed = 202210, sim = 10) # with DIP OneSamplePoisson.Design(list(1,0,0), nmin = 10, nmax=100, m0 = 5, m1 = 4, d = 0, ps = 0.95, pf = 0.05, power = 0.80, t1error=0.05, alternative = "less", seed = 202210, sim = 10)
For a given planned sample size, the efficacy and futility boundaries, return the power, the type I error, the expected sample size and its standard deviation, the probability of reaching the efficacy and futility boundaries. Equal allocation between two treatment groups.
TwoSampleBernoulli( prior, N = 200, p1, p2, d = 0, ps = 0.95, pf = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 5000 )TwoSampleBernoulli( prior, N = 200, p1, p2, d = 0, ps = 0.95, pf = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 5000 )
prior |
A list of length 3 containing the distributional information of the prior. The first element is a number specifying the type of prior. Options are
The second and third elements of the list are the parameters a and b, respectively. |
N |
The total planned sample size for two treatment groups. |
p1 |
The response rate of the new treatment. |
p2 |
The response rate of the compared treatment. |
d |
The target improvement (minimal clinically meaningful difference). |
ps |
The efficacy boundary (upper boundary). |
pf |
The futility boundary (lower boundary). |
alternative |
less (lower values imply greater efficacy) or greater (larger values imply greater efficacy). |
seed |
The seed for simulations. |
sim |
The number of simulations. |
A list of the arguments with method and computed elements
# with traditional Bayesian prior Beta(1,1) TwoSampleBernoulli(list(2,1,1), N = 200, p1 = 0.5, p2 = 0.3, d = 0, ps = 0.90, pf = 0.05, alternative = "greater", seed = 202210, sim = 5) # with DIP TwoSampleBernoulli(list(1,0,0), N = 200, p1 = 0.5, p2 = 0.3, d = 0, ps = 0.90, pf = 0.05, alternative = "greater", seed = 202210, sim = 5)# with traditional Bayesian prior Beta(1,1) TwoSampleBernoulli(list(2,1,1), N = 200, p1 = 0.5, p2 = 0.3, d = 0, ps = 0.90, pf = 0.05, alternative = "greater", seed = 202210, sim = 5) # with DIP TwoSampleBernoulli(list(1,0,0), N = 200, p1 = 0.5, p2 = 0.3, d = 0, ps = 0.90, pf = 0.05, alternative = "greater", seed = 202210, sim = 5)
Calculate the minimum planned sample size under an admissible design. The users decide the power and type-I-error, and pick the efficacy and futility boundaries. If there are no admissible design based on controlled type-I-error, then default to output the designs with the lowest type-I-error and at least the user-defined (e.g. 80%) power.
TwoSampleBernoulli.Design( prior, nmin = 10, nmax = 200, p1, p2, d = 0, ps = 0.95, pf = 0.05, power = 0.8, t1error = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 500 )TwoSampleBernoulli.Design( prior, nmin = 10, nmax = 200, p1, p2, d = 0, ps = 0.95, pf = 0.05, power = 0.8, t1error = 0.05, alternative = c("less", "greater"), seed = 202209, sim = 500 )
prior |
A list of length 3 containing the distributional information of the prior. The first element is a number specifying the type of prior. Options are
The second and third elements of the list are the parameters a and b, respectively. |
nmin |
The start searching total sample size for two treatment groups. |
nmax |
The stop searching total sample size for two treatment groups. |
p1 |
The response rate of the new treatment. |
p2 |
The response rate of the compared treatment. |
d |
The target improvement (minimal clinically meaningful difference). |
ps |
The efficacy boundary (upper boundary). |
pf |
The futility boundary (lower boundary). |
power |
The power to achieve. |
t1error |
The controlled type-I-error. |
alternative |
less (lower values imply greater efficacy) or greater (larger values imply greater efficacy). |
seed |
The seed for simulations. |
sim |
The number of simulations. |
A list of the arguments with method and computed elements
# with traditional Bayesian prior Beta(1,1) TwoSampleBernoulli.Design(list(2,1,1), nmin = 100, nmax = 120, p1 = 0.5, p2 = 0.3, d = 0, ps = 0.90, pf = 0.05, power = 0.8, t1error = 0.05, alternative = "greater", seed = 202210, sim = 10) # with DIP TwoSampleBernoulli.Design(list(1,0,0), nmin = 100, nmax = 120, p1 = 0.5, p2 = 0.3, d = 0, ps = 0.90, pf = 0.05, power = 0.8, t1error = 0.05, alternative = "greater", seed = 202210, sim = 10)# with traditional Bayesian prior Beta(1,1) TwoSampleBernoulli.Design(list(2,1,1), nmin = 100, nmax = 120, p1 = 0.5, p2 = 0.3, d = 0, ps = 0.90, pf = 0.05, power = 0.8, t1error = 0.05, alternative = "greater", seed = 202210, sim = 10) # with DIP TwoSampleBernoulli.Design(list(1,0,0), nmin = 100, nmax = 120, p1 = 0.5, p2 = 0.3, d = 0, ps = 0.90, pf = 0.05, power = 0.8, t1error = 0.05, alternative = "greater", seed = 202210, sim = 10)