basksim (version 2.0.2)

get_results.fujikawa: Get Results for Simulation of a Basket Trial with Fujikawa's Design

Description

Get Results for Simulation of a Basket Trial with Fujikawa's Design

Usage

# S3 method for fujikawa
get_results(
  design,
  n,
  p1 = NULL,
  lambda,
  epsilon,
  tau,
  logbase = 2,
  iter = 1000,
  data = NULL,
  ...
)

Value

A matrix of results with iter rows. A 0 means, that the null hypothesis that the response probability exceeds p0 was not rejected, a 1 means, that the null hypothesis was rejected.

Arguments

design

An object of class fujikawa.

n

The sample sizes of the baskets. A vector must be used for varying sample sizes.

p1

Probabilities used for the simulation. If NULL then all probabilities are set to p0.

lambda

The posterior probability threshold.

epsilon

Tuning parameter that determines the amount of borrowing. See setup_fujikawa).

tau

Tuning parameter that determines how similar the baskets have to be that information is shared. See setup_fujikawa).

logbase

Tuning parameter. The base of the logarithm that is used to calculate the Jensen-Shannon divergence.

iter

The number of iterations in the simulation. Is ignored if data is specified.

data

A data matrix with k column with the number of responses for each basket. Has to be generated with get_data. If data is used, then iter is ignored.

...

Further arguments.

Examples

design <- setup_fujikawa(k = 3, p0 = 0.2)

# Equal sample sizes
get_results(design = design, n = 20, p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, epsilon = 2, tau = 0, iter = 100)

# Unequal sample sizes
get_results(design = design, n = c(15, 20, 25), p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, epsilon = 2, tau = 0, iter = 100)