pmax_normal {RARtrials} | R Documentation |
Posterior Probability that a Particular Arm is the Best for Continuous Endpoint with Known Variances
Description
Calculate posterior probability that a particular arm is the best in a trial using Bayesian response-adaptive randomization with
a control group (the Thall Wathen method). The conjugate prior distributions follow Normal (
) distributions for
continuous outcomes with known variance in each arm and can be specified individually.
Usage
pmax_normal(
armn,
mean1 = NULL,
sd1 = NULL,
mean2 = NULL,
sd2 = NULL,
mean3 = NULL,
sd3 = NULL,
mean4 = NULL,
sd4 = NULL,
mean5 = NULL,
sd5 = NULL,
side,
...
)
Arguments
armn |
number of arms in the trial with values up to 5. When |
mean1 , sd1 |
mean and sd in Normal(mean,sd) for the arm to calculate the allocation probability of. |
mean2 , sd2 |
mean and sd in Normal(mean,sd) for one of the remaining arms. |
mean3 , sd3 |
mean and sd in Normal(mean,sd) for one of the remaining arms. |
mean4 , sd4 |
mean and sd in Normal(mean,sd) for one of the remaining arms. |
mean5 , sd5 |
mean and sd in Normal(mean,sd) for one of the remaining arms. |
side |
direction of a one-sided test, with values 'upper' or 'lower'. |
... |
additional arguments to be passed to |
Details
This function calculates the results of formula for
side
equals to 'upper' and the results of formula for
side
equals to 'lower'. This function returns the probability that the posterior probability of arm
is maximal or minimal in trials with up to five arms.
Value
a probability that a particular arm is the best in trials up to five arms.
Examples
pmax_normal(armn=5,mean1=0.8,sd1=0.2,mean2=0.5,sd2=0.1,mean3=0.8,
sd3=0.5,mean4=0.6,sd4=0.2,mean5=0.6,sd5=0.2,side='upper')
pmax_normal(armn=4,mean1=8,sd1=2,mean2=8.5,sd2=2,mean3=8.3,
sd3=1.8,mean4=8.7,sd4=2,side='lower')
pmax_normal(armn=3,mean1=80,sd1=20,mean2=50,sd2=10,mean3=80,
sd3=15,side='upper')