Description Usage Arguments Value References Examples
The design function to sequentially monitor sample size and boundary based on Thall and Simon's criterion.
1 | PostP.design(type, nmax, a, b, p0, 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 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. |
boundset |
the boundaries set: U_n or L_n |
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.
1 2 3 4 5 6 | ## 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)
|
Loading required package: animation
Loading required package: nleqslv
n bound
1 1 NA
2 2 NA
3 3 NA
4 4 NA
5 5 NA
6 6 NA
7 7 NA
8 8 0
9 9 0
10 10 0
11 11 0
12 12 0
13 13 1
14 14 1
15 15 1
16 16 1
17 17 1
18 18 2
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21 21 2
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n bound
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n bound
1 1 NA
2 2 NA
3 3 NA
4 4 NA
5 5 NA
6 6 0
7 7 0
8 8 0
9 9 0
10 10 0
11 11 0
12 12 1
13 13 1
14 14 1
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n bound
1 1 1
2 2 2
3 3 2
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