baskexact (version 1.0.1)

ecd: Expected number of correct decisions

Description

Computes the expected number of correct decisions of a basket trial.

Usage

ecd(design, ...)
# S4 method for OneStageBasket
ecd(
  design,
  p1 = NULL,
  n,
  lambda,
  weight_fun,
  weight_params = list(),
  globalweight_fun = NULL,
  globalweight_params = list(),
  ...
)
# S4 method for TwoStageBasket
ecd(
  design,
  p1 = NULL,
  n,
  n1,
  lambda,
  interim_fun,
  interim_params = list(),
  weight_fun,
  weight_params = list(),
  globalweight_fun = NULL,
  globalweight_params = list(),
  ...
)

Value

A numeric value.

Arguments

design

An object of class Basket created by setupOneStageBasket or setupTwoStageBasket.

...

Further arguments.

p1

Probabilities under the alternative hypothesis. If length(p1) == 1, then this is a common probability for all baskets.

n

The sample size per basket.

lambda

The posterior probability threshold. See details for more information.

weight_fun

Which function should be used to calculate the pairwise weights.

weight_params

A list of tuning parameters specific to weight_fun.

globalweight_fun

Which function should be used to calculate the global weights.

globalweight_params

A list of tuning parameters specific to globalweight_fun.

n1

The sample size per basket for the interim analysis in case of a two-stage design.

interim_fun

Which type of interim analysis should be conducted in case of a two-stage design.

interim_params

A list of tuning parameters specific to interim_fun.

Methods (by class)

• ecd(OneStageBasket): Expected number of correction decisions for a single-stage basket design.

• ecd(TwoStageBasket): Expected number of correction decisions for a two-stage basket design.

Details

Computes the expected number of correction decisions, i.e. the expected number of actually active baskets that are declared active and actually inactive baskets that are declared inactive.

Examples

design <- setupOneStageBasket(k = 3, p0 = 0.2)
ecd(design = design, p1 = c(0.5, 0.2, 0.2), n = 20, lambda = 0.99,
weight_fun = weights_fujikawa)