The whole design with double thresholds showing futility and efficacy boundary together

Description

The design function to sequentially monitor sample size and stopping boundary for both futility and efficacy

Usage

1
DT.design(type, a, b, nmin, nmax, p0, p1, theta0, theta1, theta_t, optimize)

Arguments

type

type of stopping criterion: "PostP" or "PredP".

a

the hyperparameter (shape1) of the Beta prior for the experimental drug.

b

the hyperparameter (shape2) of the Beta prior for the experimental drug.

nmin

the minimum number of patients treated by the experimental drug.

nmax

the maximum number of patients treated by the experimental drug.

p0

the pre-specified reseponse rate.

p1

the pre-specified reseponse rate.

theta0

the cutoff probability for futility: typically, θ_0 = [0.01, 0.1].

theta1

the cutoff probability for efficacy: typically, θ_1 = [0.9, 0.99].

theta_t

the cutoff probability for efficacy including future patients; typically, θ_T = [0.85, 0.95]. Set 0.9 by default.

optimize

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

Value

boundsets

the boundaries sets: U_n and L_n

References

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

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
## Using vague prior Unif(0,1), sequential monitor
DT.design(type = "PostP", a=1, b=1, nmin=20, nmax=60, p0=0.4, p1=0.3, theta0 = 0.05, theta1 = 0.9)
DT.design(type = "PredP", a=1, b=1, nmin=20, nmax=60, p0=0.4, p1=0.3, theta0 = 0.05, theta1 = 0.9,
theta_t = 0.9)
## Or using Jeffery prior with Beta(0.5,0.5), multi-stage monitor when sample size is
## 10, 20, ..., 80
DT.design(type = "PostP", a=0.5, b=0.5, nmin=1, nmax=85, p0=0.3, p1=0.3, theta0 = 0.05,
theta1 = 0.9)[(1:8)*10,]
DT.design(type = "PredP", a=0.5, b=0.5, nmin=1, nmax=85, p0=0.3, p1=0.3, theta0 = 0.05,
theta1 = 0.9, theta_t = 0.9)[(1:8)*10,]