Bayesian design method for sequentially monitoring patients using Beta-Binomial posterior probability based on observing data

Description

Make animation plots to present sequential monitor stopping rule using Beta-Binomial Bayesian model

Usage

1
2
bayes.design(a,b,r=0, stop.rule="futility", add.size=5, alpha=0.05,
p0 ,delta=0.2,tau1=0.9,tau2=0.9,tau3=0.9,tau4=0.9, time.interval =1)

Arguments

a

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

b

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

r

the maximum number of patients treated by the experimental drug.

stop.rule

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

add.size

a single integer value, random number generator (RNG) state for random number generation.

alpha

the siginificant level to determine the credible interval, set 0.05 by default.

p0

the prespecified reseponse rate.

delta

the minimally acceptable increment of the response rate.

tau1

threshold for stopping rule 1.

tau2

threshold for stopping rule 2.

tau3

threshold for stopping rule 3.

tau4

threshold for stopping rule 4.

time.interval

a positive number to set the time interval of the animation (unit in seconds); default to be 1.

Value

animation plot of determination of stopping boundaries.

References

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

Examples

1
2
3
4
5
6
7
8
9
# Using Multiple Myeloma (MM) data example
MM.r = rep(0,6); MM.mean = 0.1; MM.var = 0.0225
a <- MM.mean^2*(1-MM.mean)/MM.var - MM.mean; b <- MM.mean*(1-MM.mean)^2/MM.var - (1-MM.mean)
bayes.design(a=a,b=b,r=MM.r,stop.rule="futility",p0=0.1)

# Using Acute Promyelocytic Leukaemia (APL) data example
APL.r <- c(0,1,0,0,1,1); APL.mean = 0.3; APL.var = 0.0191
a <- APL.mean^2*(1-APL.mean)/APL.var - APL.mean; b <- APL.mean*(1-APL.mean)^2/APL.var - (1-APL.mean)
bayes.design(a=a,b=b,r=APL.r,stop.rule="efficacy",p0=0.1)