TwoSampleBernoulli.Design: Two sample Bernoulli model - Trial Design

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

Two sample Bernoulli 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

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
)

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 ; Beta(a,b), where a = shape, b = scale 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.

Value

A list of the arguments with method and computed elements

Examples

# 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)

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