OneSamplePoisson.Design: One sample Poisson model - Trial Design

In BayesDIP: Bayesian Decreasingly Informative Priors for Early Termination Phase II Trials

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 DIP ; Gamma(a,b), where a = shape, b = rate 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)

BayesDIP documentation built on Feb. 16, 2023, 10:09 p.m.