interim_posterior {baskexact}

Interim analysis based on the posterior probability

Description

Conducts an interim analysis based on the posterior probability.

Usage

interim_posterior(design, ...)

## S4 method for signature 'TwoStageBasket'
interim_posterior(
  design,
  n1,
  r1,
  weight_mat,
  globalweight_fun = NULL,
  globalweight_params = list(),
  prob_futstop = 0.1,
  prob_effstop = 0.9,
  ...
)

Arguments

design

An object of class Basket created by setupOneStageBasket or setupTwoStageBasket.

...

Further arguments.

n1

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

r1

Vector of responses after the interim analysis.

weight_mat

The matrix with all weights. Automatically calculated in the functions to which interim_postpred is passed.

globalweight_fun

Which function should be used to calculate the global weights.

globalweight_params

A list of tuning parameters specific to globalweight_fun.

prob_futstop

Probability cut-off for stopping for futility.

prob_effstop

Probability cut-off for stopping for efficacy.

Details

interim_posterior conducts an interim analysis with possible stop for efficacy and futility based on the posterior probability. If the posterior probability is less than prob_fustop the basket is stopped for futility, if the posterior probability is greater than prob_effstop the basket is stopped for efficacy. If prob_fustop = 0 or prob_effstop = 1 then no futility-stop and no efficacy stop is possible, respectively.

The function is generally not called by the user but passed to another function such as toer and pow to specify which interim analysis is conducted.

Value

A vector with a length equal to the number of baskets with elements -1, 0 or 1 where -1 means stop for futility, 0 means continuation and 1 means stop for efficacy.

Methods (by class)

• interim_posterior(TwoStageBasket): Interim analysis based on the posterior probabilty for two-stage basket designs.

Examples

design <- setupTwoStageBasket(k = 3, p0 = 0.2)
toer(design, n = 20, n1 = 10, lambda = 0.99, weight_fun = weights_fujikawa,
  interim_fun = interim_posterior, interim_params = list(prob_futstop = 0.05,
    prob_effstop = 0.95))