PostP.design: The stopping boundaries based on the posterior probability...

Description Usage Arguments Value References Examples

Description

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

Usage

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PostP.design(type, nmax, a, b, p0, 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 pre-specified reseponse rate.

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

Thall, P. F., Simon, R. (1994). Practical Bayesian guidelines for phase IIB clinical trials. Biometrics 50: 337-349.

Yin, G. (2012). Clinical Trial Design: Bayesian and Frequentist Adaptive Methods. New York: Wiley.

Examples

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## Using vague prior Unif(0,1)
PostP.design(type = "futility", nmax=100, a=1, b=1, p0=0.3, theta=0.05)
PostP.design(type = "efficacy", nmax=100, a=1, b=1, p0=0.3, theta=0.9)
## Or using Jeffery prior with Beta(0.5,0.5)
PostP.design(type = "futility", nmax=100, a=0.5, b=0.5, p0=0.3, theta=0.05)
PostP.design(type = "efficacy", nmax=100, a=0.5, b=0.5, p0=0.3, theta=0.9)


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