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

## Description

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

## Usage

 `1` ```PredP.design(type, nmax, a, b, p0, theta_t, 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 the response rate for the standard drug. `theta_t` the cutoff probability for efficacy including future patients; typically, θ_T = [0.85, 0.95]. Set 0.9 by default. `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

Lee, J. J., Liu, D. D. (2008). A predictive probability design for phase II cancer clinical trials. Clinical Trials 5: 93-106.

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

## Examples

 ```1 2``` ```PredP.design(type = "futility", nmax=40, a=1, b=1, p0=0.3, theta=0.05) PredP.design(type = "efficacy", nmax=40, a=1, b=1, p0=0.3, theta=0.9) ```

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