check_mon_within {baskexact}R Documentation

Check Within-Trial Monotonicity

Description

Checks whether the within-trial monotonicity condition holds.

Usage

check_mon_within(
  design,
  n,
  lambda,
  epsilon,
  tau,
  logbase = 2,
  prune,
  details,
  ...
)

## S4 method for signature 'OneStageBasket'
check_mon_within(
  design,
  n,
  lambda,
  epsilon,
  tau,
  logbase = 2,
  prune,
  details,
  ...
)

Arguments

design

An object of class Basket created by setupBasket.

n

The sample size per basket.

lambda

The posterior probability threshold. See details for more information.

epsilon

A tuning parameter that determines the amount of borrowing. See details for more information.

tau

A tuning parameter that determines how similar the baskets have to be that borrowing occurs. See details for more information.

logbase

A tuning parameter that determines which logarithm base is used to compute the Jensen-Shannon divergence. See details for more information.

prune

Whether baskets with a number of responses below the critical pooled value should be pruned before the final analysis.

details

Whether the cases where the monotonicity condition is violated should be returned, in case there are any.

...

Further arguments.

Details

check_mon_within checks whether the within-trial monotonicity condition holds. For a single-stage design with equal prior distributions and equal sample sizes for each basket this condition states that there are no cases where the null hypothesis of a basket is rejected when there is at least one other basket with more observed responses for which the null hypothesis cannot be rejected.

If prune = TRUE then the baskets with an observed number of baskets smaller than the pooled critical value are not borrowed from. The pooled critical value is the smallest integer c for which all null hypotheses can be rejected if the number of responses is exactly c for all baskets.

This method is implemented for the class OneStageBasket.

Value

If details = FALSE then only a logical value is returned. If details = TRUE then if there are any cases where the within-trial monotonicity condition is violated, a list of these cases and their results are returned.

Methods (by class)

Examples

design <- setupOneStageBasket(k = 4, shape1 = 1, shape2 = 1, theta0 = 0.2)
check_mon_within(design = design, n = 24, lambda = 0.99, epsilon = 0.5,
  tau = 0, prune = FALSE, details = TRUE)

[Package baskexact version 0.1.0 Index]