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

 `1` ```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

 ```1 2``` ```# Using vague prior Uniform(0,1), i.e. Beta(1,1) PredP(16, 23, 40, 1, 1, 0.5, 0.9) ```

Example output

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