ecd: Calculate the Expected Number of Correct Decisions for a...

In basksim: Simulation-Based Calculation of Basket Trial Operating Characteristics

Calculate the Expected Number of Correct Decisions for a Basket Trial Design

Description

Calculate the Expected Number of Correct Decisions for a Basket Trial Design

Usage

ecd(
  design,
  n,
  p1,
  lambda,
  design_params = list(),
  iter = 1000,
  data = NULL,
  ...
)

Arguments

design	An object created with one of the setup functions.
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.
design_params	A list of params that is specific to the class of design.
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.

Value

A numeric value.

Examples

# Example for a basket trial with Fujikawa's Design
design <- setup_fujikawa(k = 3, p0 = 0.2)

# Equal sample sizes
ecd(design = design, n = 20, p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, design_params = list(epsilon = 2, tau = 0),
  iter = 1000)

# Unequal sample sizes
ecd(design = design, n = c(15, 20, 25), p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, design_params = list(epsilon = 2, tau = 0),
  iter = 1000)

basksim documentation built on May 12, 2026, 9:08 a.m.