baskexact (version 1.0.1)

estim: Posterior Mean and Mean Squared Error

Description

Computes the posterior mean and the mean squared error of a basket trial design.

Usage

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

Value

A list containing means of the posterior distributions and the mean squared errors for all baskets.

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)

• estim(OneStageBasket): Posterior mean and mean squared error for a single-stage basket design.

• estim(TwoStageBasket): Posterior mean and mean squared error for a two-stage basket design.

Examples

design <- setupOneStageBasket(k = 3, p0 = 0.2)
estim(design = design, p1 = c(0.2, 0.2, 0.5), n = 15,
  weight_fun = weights_fujikawa)