des_bayesfreq: Design a Bayesian-frequentist single-arm trial for a single...
In mjg211/singlearm: Design and analysis of single-arm clinical trials

Description Usage Arguments Details Value See Also Examples

Determines optimised single- and two-stage Bayesian-frequentst single-arm clinical trial designs for a single binary primary endpoint, using exact calculations.

des_bayesfreq(J = 2, pi0 = 0.1, pi1 = 0.3, alpha = 0.05,
  beta = 0.2, mu = 0.1, nu = 0.9, Nmin = 1, Nmax = 30,
  optimality = "ess", control = c("frequentist", "bayesian"),
  equal_n = F, PL = 0.5, PU = 0.9, PT = 0.95, summary = F)

`J`	The maximal number of stages to allow.
`pi0`	The (undesirable) response probability used in the definition of the null hypothesis.
`pi1`	The (desiable) response probability used in the definition of the alternative hypothesis.
`alpha`	The desired maximal type-I error-rate.
`beta`	The desired maximal type-II error-rate.
`mu`	The first shape parameter of the Beta distribution.
`nu`	The second shape parameter of the Beta distribution.
`Nmin`	The minimal total sample size to allow in considered designs.
`Nmax`	The maximal total sample size to allow in considered designs.
`optimality`	Choice of optimal design criteria. Must be one of `"ess"` or `"minimax"`.
`control`	Error-rates to control. Should be a vector containing elements chosen from "frequentist" and "bayesian".
`equal_n`	A logical variable indicating that the sample size of each stage should be equal.
`PL`	Predictive probability value used in determining when to stop the trial early for futility.
`PU`	Predictive probability value used in determining when to stop the trial early for efficacy.
`PT`	Terminal predictie probability value used in determining when the trial is a success.
`summary`	A logical variable indicating a summary of the function's progress should be printed to the console.

Designs controlling Bayesian, frequentist, or Bayesian and frequentist operating characteristics can be determining, which optimise either the Bayesian expected sample size or the maximal sample size.

A list of class "sa_des_bayesfreq" containing the following elements

A list in the slot $des containing details of the identified optimal design.
A tibble in the slot $feasible, consisting of the identified designs which met the required operating characteristics.
Each of the input variables as specified.

opchar_bayesfreq, and their associated plot family of functions.

# The ESS-optimal design for the default parameters
ess_optimal <- des_bayesfreq()
# The corresponding minimax design
minimax     <- des_bayesfreq(optimality = "minimax")