predprob: The predictive probability criterion function

In ph2bayes: Bayesian Single-Arm Phase II Designs

Description

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

Usage

predprob(y, n, nmax, alpha_e, beta_e, p_s, theta_t)

Arguments

y	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.
alpha_e	the hyperparameter (shape1) of the Beta prior for the experimental drug.
beta_e	the hyperparameter (shape2) of the Beta prior for the experimental drug.
p_s	the the response rate for the standard drug.
theta_t	the prespecified target probability; tipically, θ_T = [0.85, 0.95].

Value

prob	the predictive probability: PP = ∑_{x=0}^{n_{max}-n} P(x | y) I(\Pr(p_E > p_S | 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

# p. 97, PP = 0.5656
predprob(16, 23, 40, 0.6, 0.4, 0.6, 0.9)

Example output

[1] 0.5655589

ph2bayes documentation built on May 2, 2019, 3:20 p.m.