OneSamplePoisson {BayesDIP} R Documentation

## One sample Poisson 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.

### Usage

OneSamplePoisson(
prior,
N = 100,
m0,
m1,
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 DIP ; Gamma(a,b), where a = shape, b = rate 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.

### Value

A list of the arguments with method and computed elements

### Examples

# 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)


[Package BayesDIP version 0.1.1 Index]