The stopping boundaries based on the predictive probability criterion

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Description

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

Usage

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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

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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)