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
)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.
The maximum number of subjects allowed in a dose cohort.
The target toxicity probability. Defaults to 0.3.
The lower equivalence limit for the target toxicity probability.
The upper equivalence limit for the target toxicity probability.
The prior toxicity shape parameter for the Beta prior.
The prior non-toxicity shape parameter for the Beta prior.
The threshold for excessive toxicity.
If the posterior probability that the true toxicity rate exceeds
pT is greater than pExcessTox, the current and
all higher doses will be excluded from further use to protect
future participants. Defaults to 0.95.
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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