BOINTable {lrstat}

BOIN decision table

Description

Obtains the decision table for the Bayesian optimal interval (BOIN) design.

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 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.

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: 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.

Author(s)

Kaifeng Lu, [e-mail]

Examples

BOINTable(nMax = 18, pT = 0.3, phi = 0.6*0.3, phi2 = 1.4*0.3)