| bayes.design {ph2bye} | R Documentation |
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
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
# 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)