*! version 1.0 14 May 2018

Title

des_bayesfreq -- Design a Bayesian-frequentist single-arm trial for a single binary endpoint

Syntax

des_bayesfreq
[,
options]

Main
j(#) The maximal number of stages to allow. Defaults to 2.

pi0(#) The (undesirable) response probability used in the definition of the null hypothesis. Defaults to 0.1.

pi1(#) The (desirable) response probability at which the trial is powered. Defaults to 0.3.

alpha(#) The desired maximal type-I error-rate. Defaults to 0.05.

beta(#) The desired maximal type-II error-rate. Defaults to 0.2.

mu(#) The first shape parameter of the Beta distribution. Defaults to 0.1.

nu(#) The second shape parameter of the Beta distribution. Defaults to 0.9.

nmin(#) The minimal total sample size to allow in considered designs. Defaults to 1.

nmax(#) The maximal total sample size to allow in considered designs. Defaults to 30.

optimality(string) The choice of optimal design criteria. Must be one of ess or minimax. Defaults to ess.

control(string) Error-rates to control. Should be either frequentist, bayesian, or both. Defaults to both.

equal_n(#) An integer indicating that the sample size of each stage should be equal (equal = 1). Default value is 0.

pl(#) Predictive probability value used in determining when to stop the trial early for futility. Defaults to 0.5.

pu(#) Predictive probability value used in determining when to stop the trial early for futility. Defaults to 0.9.

pt(#) Terminal predictive probability value used in determining when the trial is a success. Defaults to 0.95.

summary(#) An integer indicating whether a summary of the function's progress should be printed (summary = 1) to the console. Defaults to 0.

plot Indicates that the stopping boundaries of the identified optimal design should be plotted.

* Additional options to use during plotting.

Description

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

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.

Options

Main

j(#) The maximal number of stages to allow. Defaults to 2.

pi0(#) The (undesirable) response probability used in the definition of the
null hypothesis. Defaults to 0.1.

pi1(#) The (desirable) response probability at which the trial is powered.
Defaults to 0.3.

alpha(#) The desired maximal type-I error-rate. Defaults to 0.05.

beta(#) The desired maximal type-II error-rate. Defaults to 0.2.

mu(#) The first shape parameter of the Beta distribution. Defaults to 0.1.

nu(#) The second shape parameter of the Beta distribution. Defaults to 0.9.

nmin(#) The minimal total sample size to allow in considered designs.
Defaults to 1.

nmax(#) The maximal total sample size to allow in considered designs.
Defaults to 30.

optimality(string) The choice of optimal design criteria. Must be one of ess or minimax. Defaults to ess.

control(string) Error-rates to control. Should be either frequentist, bayesian, or both. Defaults to both.

equal_n(#) An integer indicating that the sample size of each stage should be equal (equal = 1). Default value is 0.

pl(#) Predictive probability value used in determining when to stop the trial early for futility. Defaults to 0.5.

pu(#) Predictive probability value used in determining when to stop the trial early for futility. Defaults to 0.9.

pt(#) Terminal predictive probability value used in determining when the trial is a success. Defaults to 0.95.

summary(#) An integer indicating whether a summary of the function's progress should be printed (summary = 1) to the console. Defaults to 0.

plot Indicates that the stopping boundaries of the identified optimal design should be plotted.

* Additional options to use during plotting.

Examples

des_bayesfreq

des_bayesfreq, j(1)

Author

Michael J. Grayling, MRC Biostatistics Unit, Cambridge.

Email mjg211@cam.ac.uk