View source: R/getDesignProportions.R
BOINTable | R Documentation |
Obtains the decision table for the Bayesian optimal interval (BOIN) design.
BOINTable(
nMax = NA_integer_,
pT = 0.3,
phi1 = 0.6 * pT,
phi2 = 1.4 * pT,
a = 1,
b = 1,
pExcessTox = 0.95
)
nMax |
The maximum number of subjects in a dose cohort. |
pT |
The target toxicity probability. Defaults to 0.3. |
phi1 |
The lower equivalence limit for target toxicity probability. |
phi2 |
The upper equivalence limit for target toxicity probability. |
a |
The prior toxicity parameter for the beta prior. |
b |
The prior non-toxicity parameter for the beta prior. |
pExcessTox |
The threshold for excessive toxicity, i.e., if Prob(p > pT | Data) > pExcessTox, then the current and all higher doses will be excluded and never be used again in the remainder of the trial to avoid any other subjects receiving treatment at those doses. Defaults to 0.95. |
An S3 class BOINTable
object with the following
components:
settings
: The input settings data frame with the following
variables:
nMax
: The maximum number of subjects in a dose cohort.
pT
: The target toxicity probability.
phi1
: The lower equivalence limit for target toxicity
probability.
phi2
: The upper equivalence limit for target toxicity
probability.
lambda1
: The lower decision boundary for observed toxicity
probability.
lambda2
: The upper decision boundary for observed toxicity
probability.
a
: The prior toxicity parameter for the beta prior.
b
: The prior non-toxicity parameter for the beta prior.
pExcessTox
: The threshold for excessive toxicity.
decisionDataFrame
: The decision data frame for the BOIN design.
It includes the following variables:
n
: The sample size.
y
: The number of toxicities.
decision
: The dosing decision.
decisionMatrix
: The decision matrix corresponding to the
decision data frame.
Kaifeng Lu, kaifenglu@gmail.com
BOINTable(nMax = 18, pT = 0.3, phi = 0.6*0.3, phi2 = 1.4*0.3)
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