The stopping boundaries based on the multiple outcomes criterion

Description

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

Usage

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MultPostP.design(type, nmax, a.vec, p0, theta, optimize)

Arguments

type

type of boundaries: "efficacy" or "futility" or "toxicity".

nmax

the maximum number of patients treated by the experimental drug.

a.vec

the hyperparameter vector of the Dirichlet prior for the experimental drug.

p0

the prespecified reseponse rate for efficacy or toxicity.

theta

the cutoff probability: typically, θ = [0.9, 0.99] for efficacy, θ = [0.01, 0.1] for futility, and θ = [0.95, 1] for toxicity.

optimize

logical value, if optimize=TRUE, then only output the minimal sample size for the same number of futility boundaries and maximal sample size for the same number efficacy boundaries

Value

boundset

the boundaries set: U_n or L_n for the experimental drug efficacy or futility; T_n for the experimental drug toxicity.

References

Thall, Peter F., Richard M. Simon, and Elihu H. Estey. (1995). Bayesian sequential monitoring designs for single-arm clinical trials with multiple outcomes. Statistics in medicine 14.4: 357-379.

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)
MultPostP.design(type="futility",nmax = 30,a.vec = c(1,1,1,1),p0 = 0.15, theta = 0.05)
MultPostP.design(type="efficacy",nmax = 30,a.vec = c(1,1,1,1),p0 = 0.15, theta = 0.9)
MultPostP.design(type="toxicity",nmax = 30,a.vec = c(1,1,1,1),p0 = 0.15, theta = 0.95)