View source: R/getDesignProportions.R
BOINTable | R Documentation |
Generates the decision table for the Bayesian Optimal Interval (BOIN) design, a widely used approach for dose-escalation trials that guides dose-finding decisions based on observed toxicity rates.
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 allowed in a dose cohort. |
pT |
The target toxicity probability. Defaults to 0.3. |
phi1 |
The lower equivalence limit for the target toxicity probability. |
phi2 |
The upper equivalence limit for the target toxicity probability. |
a |
The prior toxicity shape parameter for the Beta prior. |
b |
The prior non-toxicity shape parameter for the Beta prior. |
pExcessTox |
The threshold for excessive toxicity.
If the posterior probability that the true toxicity rate exceeds
|
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
: A data frame listing dose-finding decisions
for each combination of sample size (n
) and number of observed
toxicities (y
):
n
: Cohort size.
y
: Number of observed toxicities.
decision
: Recommended action: escalate, de-escalate,
or stay at the current dose.
decisionMatrix
: A matrix version of the decision table
showing the recommended action based on the number of toxicities
for each possible cohort size.
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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