Description Usage Arguments Details Value See Also Examples
View source: R/des_bayesfreq.R
Determines optimised single- and two-stage Bayesian-frequentst single-arm clinical trial designs for a single binary primary endpoint, using exact calculations.
1 2 3 4 | 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
|
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.
1 2 3 4 | # The ESS-optimal design for the default parameters
ess_optimal <- des_bayesfreq()
# The corresponding minimax design
minimax <- des_bayesfreq(optimality = "minimax")
|
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