Description Usage Arguments Value References Examples
The design function to sequentially monitor sample size and boundary based on Lee and Liu's criterion.
1 | PredP.design(type, nmax, a, b, p0, theta_t, theta, optimize)
|
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. |
boundset |
the boundaries set: U_n or L_n |
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.
1 2 | 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)
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n bound
1 1 NA
2 2 NA
3 3 NA
4 4 NA
5 5 0
6 6 0
7 7 0
8 8 0
9 9 1
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n bound
1 1 NA
2 2 2
3 3 3
4 4 3
5 5 4
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