PredP: The predictive probability criterion function for Phase II...

In ph2bye: Phase II Clinical Trial Design Using Bayesian Methods

Description

Lee and Liu's criterion function for determining the trial decision cutoffs based on the predictive probability.

Usage

PredP(x, n, nmax, a, b, p0, theta_t)

Arguments

x	the number of responses among n patients treated by the experimental drug at a certain stage of the trial.
n	the number of patients treated by the experimental drug at a certain stage of the trial.
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.

Value

prob	the predictive probability: PP = ∑\limits_{y=0}^{n_{max}-n} Pr(Y=y | x) I(\Pr(p > p_0 | Y=y, x) ≥q θ_T)

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

# Using vague prior Uniform(0,1), i.e. Beta(1,1)
PredP(16, 23, 40, 1, 1, 0.5, 0.9)

Example output

Loading required package: animation
sh: 1: cannot create /dev/null: Permission denied
sh: 1: cannot create /dev/null: Permission denied
Loading required package: nleqslv
[1] 0.8871897

ph2bye documentation built on May 1, 2019, 6:33 p.m.