OneSamplePoisson.Design {BayesDIP} | R Documentation |
One sample Poisson model - Trial Design
Description
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.
Usage
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
)
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. |
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. |
Value
A list of the arguments with method and computed elements
Examples
# 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)