BAC_binom {BACCT} | R Documentation |
Bayesian Augmented Control for Binary Responses
Description
Calling JAGS to implement BAC for binary responses
Usage
BAC_binom(yh, nh, n1, n2, y1.range = 0:n1, y2.range = 0:n2, n.chain = 5,
tau.alpha = 0.001, tau.beta = 0.001, prior.type = "nonmixture",
criterion.type = c("diff", "prob"), prob.threshold, sim.mode = c("full",
"express"))
Arguments
yh , nh |
Vector of the numbers of events (subjects) in the historical trial(s). Must be of equal length. |
n1 , n2 |
Number of subjects in the control or treatment arm of the current trial. |
y1.range , y2.range |
Number of events in control or treatment arm of the current trial. See "Details". |
n.chain |
Controls the number of posterior samples. Each chain contains 20,000 samples. |
tau.alpha , tau.beta |
Hyperparameters of the inverse gamma distribution controling the extent of borrowing. |
prior.type |
Type of prior on control groups. Currenly, only the inverse-gamma prior is implemented. |
criterion.type |
Type of posterior quantities to be monitored. See "Details." |
prob.threshold |
For |
sim.mode |
Simulation duration reduces greatly in |
Details
There are two types of posterior quantities for
criterion.type
argument. With "diff"
option, the quantity
computed is p_{T} - p_{C}
; with "prob,"
such quantity is
pr(p_{T} - p_{C}>\Delta)
, where \Delta
is specified by
prob.threshold
argument.
By default, y1.range
and y2.range
cover all possible outcomes
and should be left unspecified in most cases. However, when n1
and/or n2
is fairly large, it is acceptable to use a reduced range
that covers the outcomes that are most likely (e.g., within 95% CI) to be
observed. This may help shorten the time to run MCMC.
Another way that can greatly shorten the MCMC running time is to specify
"express"
mode in sim.mode
argument. Express mode reduces the
number of simulations from length(y1.range)*length(y2.range)
to
length(y1.range)+length(y2.range)
. Express mode is proper when the
treatment arm rate is independent of control arm rate.
Value
An object of class "BAC".
Author(s)
Hongtao Zhang
Examples
## Not run:
library(BACCT)
#borrow from 3 historical trials#
yh = c(11,300,52);nh = c(45,877,128)
#specify current trial sample sizes#
n1 = 20;n2 = 30
#Difference criterion type in full simulation mode#
obj1 = BAC_binom(yh=yh,nh=nh,n1=n1,n2=n2,n.chain=5,
criterion.type="diff",sim.mode="full")
#Probability criterion type in express simulation mode#
obj2 = BAC_binom(yh=yh,nh=nh,n1=n1,n2=n2,n.chain=5,
criterion.type="prob",prob.threshold=0.1,sim.mode="express")
#S3 method for class "BAC"
summary(obj1)
## End(Not run)