TwoSampleBernoulli {BayesDIP} | R Documentation |
Two sample Bernoulli model
Description
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.
Usage
TwoSampleBernoulli(
prior,
N = 200,
p1,
p2,
d = 0,
ps = 0.95,
pf = 0.05,
alternative = c("less", "greater"),
seed = 202209,
sim = 5000
)
Arguments
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. |
Value
A list of the arguments with method and computed elements
Examples
# 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)