BOINTable {lrstat} | R Documentation |
BOIN Decision Table for Dose-Finding Trials
Description
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.
Usage
BOINTable(
nMax = NA_integer_,
pT = 0.3,
phi1 = 0.6 * pT,
phi2 = 1.4 * pT,
a = 1,
b = 1,
pExcessTox = 0.95
)
Arguments
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
|
Value
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.
Author(s)
Kaifeng Lu, kaifenglu@gmail.com
References
Liu, S., & Yuan, Y. (2015). Bayesian optimal interval designs for phase I clinical trials. Journal of the Royal Statistical Society: Series C (Applied Statistics), 64(3), 507-523.
Examples
BOINTable(nMax = 18, pT = 0.3, phi = 0.6*0.3, phi2 = 1.4*0.3)