# PostP.design: The stopping boundaries based on the posterior probability... In ph2bye: Phase II Clinical Trial Design Using Bayesian Methods

## Description

The design function to sequentially monitor sample size and boundary based on Thall and Simon's criterion.

## Usage

 `1` ```PostP.design(type, nmax, a, b, p0, theta, optimize) ```

## Arguments

 `type` type of boundaries: "efficacy" or "futility". `nmax` the maximum number of patients treated by the experimental drug. `a` the hyperparameter (shape1) of the Beta prior for the experimental drug. `b` the hyperparameter (shape2) of the Beta prior for the experimental drug. `p0` the pre-specified reseponse rate. `theta` the cutoff probability: typically, θ = [0.9, 0.99] for efficacy, θ = [0.01, 0.1] for futility. `optimize` logical value, if optimize=TRUE, then only output the minimal sample size for the same number of futility and efficacy boundaries.

## Value

 `boundset` the boundaries set: U_n or L_n

## References

Thall, P. F., Simon, R. (1994). Practical Bayesian guidelines for phase IIB clinical trials. Biometrics 50: 337-349.

Yin, G. (2012). Clinical Trial Design: Bayesian and Frequentist Adaptive Methods. New York: Wiley.

## Examples

 ```1 2 3 4 5 6``` ```## Using vague prior Unif(0,1) PostP.design(type = "futility", nmax=100, a=1, b=1, p0=0.3, theta=0.05) PostP.design(type = "efficacy", nmax=100, a=1, b=1, p0=0.3, theta=0.9) ## Or using Jeffery prior with Beta(0.5,0.5) PostP.design(type = "futility", nmax=100, a=0.5, b=0.5, p0=0.3, theta=0.05) PostP.design(type = "efficacy", nmax=100, a=0.5, b=0.5, p0=0.3, theta=0.9) ```

ph2bye documentation built on May 29, 2017, 9:08 a.m.