PredP.design {ph2bye}

The stopping boundaries based on the predictive probability criterion

Description

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

Usage

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, \theta_T = [0.85, 0.95]. Set 0.9 by default.

theta

the cutoff probability: typically, \theta = [0.9, 0.99] for efficacy, \theta = [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

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)