simonBayesSim {lrstat}

Simulation of Simon's Bayesian basket trials

Description

Obtains the simulated raw and summary data for Simon's Bayesian basket discovery trials.

Usage

simonBayesSim(
  p = NA_real_,
  accrualTime = 0L,
  accrualIntensity = NA_real_,
  stratumFraction = 1L,
  lambda = NA_real_,
  gamma = NA_real_,
  phi = NA_real_,
  plo = NA_real_,
  T = NA_real_,
  maxSubjects = NA_integer_,
  plannedSubjects = NA_integer_,
  maxNumberOfIterations = 1000L,
  maxNumberOfRawDatasets = 1L,
  seed = NA_integer_
)

Arguments

p

The vector of true response probabilities across strata.

accrualTime

A vector that specifies the starting time of piecewise Poisson enrollment time intervals. Must start with 0, e.g., c(0, 3) breaks the time axis into 2 accrual intervals: [0, 3) and [3, Inf).

accrualIntensity

A vector of accrual intensities. One for each accrual time interval.

stratumFraction

A vector of stratum fractions that sum to 1. Defaults to 1 for no stratification.

lambda

The prior probability that the drug activity is homogeneous across strata.

gamma

The prior probability that the drug is active in a stratum.

phi

The response probability for an active drug.

plo

The response probability for an inactive drug.

T

The threshold for a conclusive posterior probability to stop enrollment.

maxSubjects

The maximum total sample size.

plannedSubjects

The planned cumulative number of subjects at each stage.

maxNumberOfIterations

The number of simulation iterations. Defaults to 1000.

maxNumberOfRawDatasets

The number of raw datasets to extract.

seed

The seed to reproduce the simulation results. The seed from the environment will be used if left unspecified,

Value

A list containing the following four components:

• rawdata: A data frame for subject-level data, containing the following variables:

  • iterationNumber: The iteration number.

  • stageNumber: The stage number.

  • subjectId: The subject ID.

  • arrivalTime: The enrollment time for the subject.

  • stratum: The stratum for the subject.

  • y: Whether the subject was a responder (1) or nonresponder (0).

• sumdata1: A data frame for simulation and stratum-level summary data, containing the following variables:

  • iterationNumber: The iteration number.

  • stageNumber: The stage number.

  • stratum: The stratum number.

  • active: Whether the drug is active in the stratum.

  • n: The number of subjects in the stratum.

  • r: The number of responders in the stratum.

  • posterior: The posterior probability that the drug is active in the stratum.

  • open: Whether the stratum is still open for enrollment.

  • positive: Whether the stratum has been determined to be a positive stratum.

  • negative: Whether the stratum has been determined to be a negative stratum.

• sumdata2: A data frame for the simulation level summary data, containing the following variables:

  • iterationNumber: The iteration number.

  • numberOfStrata: The total number of strata.

  • n_active_strata: The number of active strata.

  • true_positive: The number of true positive strata.

  • false_negative: The number of false negative strata.

  • false_positive: The number of false positive strata.

  • true_negative: The number of true negative strata.

  • n_indet_strata: The number of indeterminate strata.

  • numberOfSubjects: The number of subjects.

• overview: A data frame for the summary across simulations, containing the following variables:

  • numberOfStrata: The total number of strata.

  • n_active_strata: The average number of active strata.

  • true_positive: The average number of true positive strata.

  • false_negative: The average number of false negative strata.

  • false_positive: The average number of false positive strata.

  • true_negative: The average number of true negative strata.

  • n_indet_strata: The average number of indeterminate strata.

  • numberOfSubjects: The average number of subjects.

Author(s)

Kaifeng Lu, [e-mail]

Examples

sim1 = simonBayesSim(
  p = c(0.25, 0.25, 0.05),
  accrualIntensity = 5,
  stratumFraction = c(1/3, 1/3, 1/3),
  lambda = 0.33, gamma = 0.5,
  phi = 0.25, plo = 0.05,
  T = 0.8, maxSubjects = 50,
  plannedSubjects = seq(5, 50, 5),
  maxNumberOfIterations = 1000,
  maxNumberOfRawDatasets = 1,
  seed = 314159)

sim1$overview