Type:	Package
Title:	Simulation-Based Calculation of Basket Trial Operating Characteristics
Version:	2.0.2
Description:	Provides a unified syntax for the simulation-based comparison of different single-stage basket trial designs with a binary endpoint and equal sample sizes in all baskets. Methods include the designs by Baumann et al. (2025) <doi:10.1080/19466315.2024.2402275>, Schmitt and Baumann (2025) <doi:10.1080/19466315.2025.2486231>, Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>, Berry et al. (2020) <doi:10.1177/1740774513497539>, and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>. For the latter two designs, the functions are mostly wrappers for functions provided by the package 'bhmbasket'.
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	arrangements, bhmbasket, doFuture, extraDistr, foreach, HDInterval, progressr, purrr, stats
Suggests:	covr, testthat (≥ 3.0.0), ggplot2
Config/testthat/edition:	3
URL:	https://github.com/lbau7/basksim
BugReports:	https://github.com/lbau7/basksim/issues
NeedsCompilation:	no
Packaged:	2026-02-20 12:51:48 UTC; uy240
Author:	Lukas Baumann [aut, cre], Lukas D Sauer [aut], Sabrina Schmitt [aut]
Maintainer:	Lukas Baumann <baumann@imbi.uni-heidelberg.de>
Repository:	CRAN
Date/Publication:	2026-02-20 14:50:08 UTC

basksim: Simulation-Based Calculation of Basket Trial Operating Characteristics

Description

Provides a unified syntax for the simulation-based comparison of different single-stage basket trial designs with a binary endpoint and equal sample sizes in all baskets. Methods include the designs by Baumann et al. (2025) doi:10.1080/19466315.2024.2402275, Schmitt and Baumann (2025) doi:10.1080/19466315.2025.2486231, Fujikawa et al. (2020) doi:10.1002/bimj.201800404, Berry et al. (2020) doi:10.1177/1740774513497539, and Neuenschwander et al. (2016) doi:10.1002/pst.1730. For the latter two designs, the functions are mostly wrappers for functions provided by the package 'bhmbasket'.

Author(s)

Maintainer: Lukas Baumann baumann@imbi.uni-heidelberg.de (ORCID)

Authors:

• Lukas D Sauer sauer@imbi.uni-heidelberg.de (ORCID)

• Sabrina Schmitt sabrina.schmitt@uni-wuerzburg.de (ORCID)

See Also

Useful links:

• https://github.com/lbau7/basksim

• Report bugs at https://github.com/lbau7/basksim/issues

Adjust Lambda

Description

Adjust Lambda

Usage

adjust_lambda(design, ...)

Arguments

Details

The default method for adjust_lambda uses a combination of uniroot and grid search and calls toer in every iteration. For methods implemented in the bhmbasket package there are separate methods that are computationally more efficient.

Value

A list containing the greatest estimated value for lambda with prec_digits decimal places which controls the family wise error rate at level alpha (one-sided) and the estimated family wise error rate for the estimated lambda.

Examples

design <- setup_cpp(k = 3, p0 = 0.2)

# Equal sample sizes
adjust_lambda(design = design, n = 20, alpha = 0.05,
  design_params = list(tune_a = 1, tune_b = 1), iter = 1000)

# Unequal sample sizes
adjust_lambda(design = design, n = c(15, 20, 25), alpha = 0.05,
   design_params = list(tune_a = 1, tune_b = 1), iter = 1000)

Adjust Lambda for the BHM Design

Description

Adjust Lambda for the BHM Design

Usage

## S3 method for class 'bhm'
adjust_lambda(
  design,
  n,
  p1 = NULL,
  alpha = 0.05,
  design_params = list(),
  iter = 1000,
  n_mcmc = 10000,
  prec_digits = 3,
  data = NULL,
  ...
)

Arguments

Value

A list containing the greatest estimated value for lambda with prec_digits decimal places which controls the family wise error rate at level alpha (one-sided) and the estimated family wise error rate for the estimated lambda.

Examples

design <- setup_bhm(k = 3, p0 = 0.2, p_target = 0.5)

# Equal sample sizes
adjust_lambda(design = design, n = 15, design_params = list(tau_scale = 1),
  iter = 100, n_mcmc = 5000)

# Unequal sample sizes
adjust_lambda(design = design, n = c(15, 20, 25),
  design_params = list(tau_scale = 1),
  iter = 100, n_mcmc = 5000)

Adjust Lambda

Description

Adjust Lambda

Usage

## Default S3 method:
adjust_lambda(
  design,
  n,
  p1 = NULL,
  alpha = 0.05,
  design_params = list(),
  iter = 1000,
  prec_digits = 3,
  data = NULL,
  ...
)

Arguments

Details

It is recommended to use data and then use the same simulated data set for all further calculations. If data = NULL then new data are generated in each step of the algorithm, so lambda doesn't necessarily protect the family wise error rate for different simulated data due to Monte Carlo simulation error.

Value

A list containing the greatest estimated value for lambda with prec_digits decimal places which controls the family wise error rate at level alpha (one-sided) and the estimated family wise error rate for the estimated lambda.

Examples

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

# Equal sample sizes
adjust_lambda(design = design, n = 20, alpha = 0.05,
  design_params = list(epsilon = 2, tau = 0), iter = 1000)

# Unequal sample sizes
adjust_lambda(design = design, n = c(15, 20, 25), alpha = 0.05,
  design_params = list(epsilon = 2, tau = 0), iter = 1000)

Adjust Lambda for the EXNEX Design

Description

Adjust Lambda for the EXNEX Design

Usage

## S3 method for class 'exnex'
adjust_lambda(
  design,
  n,
  p1 = NULL,
  alpha = 0.05,
  design_params = list(),
  iter = 1000,
  n_mcmc = 10000,
  prec_digits = 3,
  data = NULL,
  ...
)

Arguments

Value

A list containing the greatest estimated value for lambda with prec_digits decimal places which controls the family wise error rate at level alpha (one-sided) and the estimated family wise error rate for the estimated lambda.

Examples

design <- setup_exnex(k = 3, p0 = 0.2)

# Equal sample sizes
adjust_lambda(design = design, n = 15,
  design_params = list(tau_scale = 1, w = 0.5),
  iter = 100, n_mcmc = 5000)

# Unequal sample sizes
adjust_lambda(design = design, n = c(15, 20, 25),
  design_params = list(tau_scale = 1, w = 0.5),
  iter = 100, n_mcmc = 5000)
  

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

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)

Plot a Bayesian basket trial's posterior distribution after borrowing

Description

Plot a Bayesian basket trial's posterior distribution after borrowing

Usage

geom_borrow(design, ...)

Arguments

Value

A list of ggplot layers of type 'geom_function'.

Examples

# Example for a basket trial with Fujikawa's Design
design <- setup_fujikawa(k = 3, p0 = 0.2)
n <- 20
r <- c(4, 5, 2)
epsilon <- 2
tau <- 0.5
# One facet per basket
library(ggplot2)
ggplot() +
    geom_borrow(design, n, r, epsilon, tau, logbase = exp(1)) +
    facet_wrap(vars(basket))
# Colour different baskets
ggplot() +
    geom_borrow(design, n, r, epsilon, tau,
                logbase = exp(1), aes(colour = basket))

Plot a Fujikawa basket trial's posterior distribution after borrowing

Description

Plot a Fujikawa basket trial's posterior distribution after borrowing

Usage

## S3 method for class 'fujikawa'
geom_borrow(design, n, r, epsilon, tau, logbase, ...)

Arguments

Value

A list of ggplot layers of type 'geom_function'.

Examples

# Example for a basket trial with Fujikawa's Design
design <- setup_fujikawa(k = 3, p0 = 0.2)
n <- 20
r <- c(4, 5, 2)
epsilon <- 2
tau <- 0.5
# One facet per basket
library(ggplot2)
ggplot() +
    geom_borrow(design, n, r, epsilon, tau, logbase = exp(1)) +
    facet_wrap(vars(basket))
# Colour different baskets
ggplot() +
    geom_borrow(design, n, r, epsilon, tau,
                logbase = exp(1), aes(colour = basket))

Plot a Bayesian basket trial's posterior distribution

Description

Plot a Bayesian basket trial's posterior distribution

Usage

geom_posterior(design, ...)

Arguments

Value

A list of ggplot layers of type 'geom_function'.

Examples

# Example for a basket trial with Fujikawa's Design
design <- setup_fujikawa(k = 3, p0 = 0.2)
n <- 20
r <- c(4, 5, 2)
# One facet per basket
library(ggplot2)
ggplot() +
    geom_posterior(design, n, r) +
    facet_wrap(vars(basket))
# Colour different baskets
ggplot() +
    geom_posterior(design, n, r, aes(colour = basket))

Plot a Fujikawa basket trial's posterior distribution

Description

Plot a Fujikawa basket trial's posterior distribution

Usage

## S3 method for class 'fujikawa'
geom_posterior(design, n, r, ...)

Arguments

Value

A list of ggplot layers of type 'geom_function'.

Examples

# Example for a basket trial with Fujikawa's Design
design <- setup_fujikawa(k = 3, p0 = 0.2)
n <- 20
r <- c(4, 5, 2)
# One facet per basket
library(ggplot2)
ggplot() +
    geom_posterior(design, n, r) +
    facet_wrap(vars(basket))
# Colour different baskets
ggplot() +
    geom_posterior(design, n, r, aes(colour = basket))

Plot a Bayesian basket trial's prior distribution

Description

Plot a Bayesian basket trial's prior distribution

Usage

geom_prior(design, ...)

Arguments

Value

A list of ggplot layers of type 'geom_function'.

Examples

# Example for a basket trial with Fujikawa's Design
design <- setup_fujikawa(k = 3, p0 = 0.2)
# One facet per basket
library(ggplot2)
ggplot() +
    geom_prior(design) +
    facet_wrap(vars(basket))
# Colour different baskets
ggplot() +
    geom_prior(design, aes(colour = basket))

Plot a Fujikawa basket trial's prior distribution

Description

Plot a Fujikawa basket trial's prior distribution

Usage

## S3 method for class 'fujikawa'
geom_prior(design, ...)

Arguments

Value

A list of ggplot layers of type 'geom_function'.

Examples

# Example for a basket trial with Fujikawa's Design
design <- setup_fujikawa(k = 3, p0 = 0.2)
# One facet per basket
library(ggplot2)
ggplot() +
    geom_prior(design) +
    facet_wrap(vars(basket))
# Colour different baskets
ggplot() +
    geom_prior(design, aes(colour = basket))

Simulate Data Based on a Binomial Distribution

Description

Simulate Data Based on a Binomial Distribution

Usage

get_data(k, n, p, iter, type = c("matrix", "bhmbasket"))

Arguments

Details

For type = "bhmbasket" this is simply a wraper for bhmbasket::simulateScenarios.

Value

If type = "matrix" then a matrix is returned, if type = "bhmbasket" then an element with class scenario_list.

Examples

# Equal sample sizes
get_data(k = 3, n = 20, p = c(0.2, 0.2, 0.5), iter = 1000,
  type = "matrix")

# Unequal sample sizes
get_data(k = 3, n = c(15, 20, 25), p = c(0.2, 0.2, 0.5),
  iter = 1000, type = "matrix")

Get Details of a Basket Trial Simulation

Description

Get Details of a Basket Trial Simulation

Usage

get_details(design, ...)

Arguments

Value

A list containing the rejection probabilities, posterior means, mean squared errors of all baskets and the family-wise error rate. For some methods the mean limits of HDI intervals are also returned.

Examples

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

# Equal sample sizes
get_details(design = design, n = 20, p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, epsilon = 2, tau = 0, iter = 100)

# Unequal sample sizes
get_details(design = design, n = c(15, 20, 25),
   p1 = c(0.2, 0.5, 0.5), lambda = 0.95, epsilon = 2,
   tau = 0, iter = 100)

Get Details of a Basket Trial Simulation with the Adaptive Power Prior Design for sequential clinical trials

Description

Get Details of a Basket Trial Simulation with the Adaptive Power Prior Design for sequential clinical trials

Usage

## S3 method for class 'app'
get_details(
  design,
  n,
  p1 = NULL,
  lambda,
  level = 0.95,
  iter = 1000,
  data = NULL,
  ...
)

Arguments

Value

A list containing the rejection probabilities, posterior means, mean squared errors and mean limits of HDI intervals for all baskets as well as the family-wise error rate.

Examples

design <- setup_app(k = 3, p0 = 0.2)

# Equal sample sizes
get_details(design = design, n = 20, p1 = c(0.2, 0.5, 0.5),
 lambda = 0.95, iter = 100)

# Unequal sample sizes
get_details(design = design, n = c(15, 20, 25), p1 = c(0.2, 0.5, 0.5),
 lambda = 0.95, iter = 100)

Get Details of a BHM Basket Trial Simulation

Description

Get Details of a BHM Basket Trial Simulation

Usage

## S3 method for class 'bhm'
get_details(
  design,
  n,
  p1 = NULL,
  lambda,
  level = 0.95,
  tau_scale,
  iter = 1000,
  n_mcmc = 10000,
  data = NULL,
  ...
)

Arguments

Value

A list containing the rejection probabilities, posterior means, mean squared errors and mean limits of HDI intervals for all baskets as well as the family-wise error rate.

Examples

design <- setup_bhm(k = 3, p0 = 0.2, p_target = 0.5)

# Equal sample sizes
get_details(design = design, n = 20, p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, tau_scale = 1, iter = 100)

# Unequal sample sizes
get_details(design = design, n = c(15, 20, 25), p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, tau_scale = 1, iter = 100)

Get Details of a Basket Trial Simulation with the Calibrated Power Prior Design

Description

Get Details of a Basket Trial Simulation with the Calibrated Power Prior Design

Usage

## S3 method for class 'cpp'
get_details(
  design,
  n,
  p1 = NULL,
  lambda,
  level = 0.95,
  tune_a,
  tune_b,
  iter = 1000,
  data = NULL,
  ...
)

Arguments

Value

A list containing the rejection probabilities, posterior means, mean squared errors and mean limits of HDI intervals for all baskets as well as the family-wise error rate.

Examples

design <- setup_cpp(k = 3, p0 = 0.2)

# Equal sample sizes
get_details(design = design, n = 20, p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, tune_a = 1, tune_b = 1, iter = 100)

# Unequal sample sizes
get_details(design = design, n = c(15, 20, 25), p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, tune_a = 1, tune_b = 1, iter = 100)

Get Details of a Basket Trial Simulation with the Global Calibrated Power Prior Design

Description

Get Details of a Basket Trial Simulation with the Global Calibrated Power Prior Design

Usage

## S3 method for class 'cppglobal'
get_details(
  design,
  n,
  p1 = NULL,
  lambda,
  level = 0.95,
  tune_a,
  tune_b,
  epsilon,
  iter = 1000,
  data = NULL,
  ...
)

Arguments

Value

A list containing the rejection probabilities, posterior means, mean squared errors and mean limits of HDI intervals for all baskets as well as the family-wise error rate.

Examples

design <- setup_cppglobal(k = 3, p0 = 0.2)
get_details(design = design, n = 20, p1 = c(0.2, 0.5, 0.5), lambda = 0.95,
  tune_a = 1, tune_b = 1, epsilon = 2, iter = 100)

Get Details of a Basket Trial Simulation with the Limited Calibrated Power Prior Design

Description

Get Details of a Basket Trial Simulation with the Limited Calibrated Power Prior Design

Usage

## S3 method for class 'cpplim'
get_details(
  design,
  n,
  p1 = NULL,
  lambda,
  level = 0.95,
  tune_a,
  tune_b,
  iter = 1000,
  data = NULL,
  ...
)

Arguments

Value

A list containing the rejection probabilities, posterior means, mean squared errors and mean limits of HDI intervals for all baskets as well as the family-wise error rate.

Examples

design <- setup_cpplim(k = 3, p0 = 0.2)

# Equal sample sizes
get_details(design = design, n = 20, p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, tune_a = 1, tune_b = 1, iter = 100)

# Unequal sample sizes
get_details(design = design, n = c(15, 20, 25), p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, tune_a = 1, tune_b = 1, iter = 100)

Get Details of a Basket Trial Simulation with the EXNEX Design

Description

Get Details of a Basket Trial Simulation with the EXNEX Design

Usage

## S3 method for class 'exnex'
get_details(
  design,
  n,
  p1 = NULL,
  lambda,
  level = 0.95,
  tau_scale,
  w,
  iter = 1000,
  n_mcmc = 10000,
  data = NULL,
  ...
)

Arguments

Value

A list containing the rejection probabilities, posterior means, mean squared errors and mean limits of HDI intervals for all baskets as well as the family-wise error rate.

Examples

design <- setup_exnex(k = 3, p0 = 0.2)

# Equal sample sizes
get_details(design = design, n = 20, p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, tau_scale = 1, w = 0.5, iter = 100)

# Unequal sample sizes
get_details(design = design, n = c(15, 20, 25), p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, tau_scale = 1, w = 0.5, iter = 100)

Get Details of a Basket Trial Simulation with Fujikawa's Design

Description

Get Details of a Basket Trial Simulation with Fujikawa's Design

Usage

## S3 method for class 'fujikawa'
get_details(
  design,
  n,
  p1 = NULL,
  lambda,
  level = 0.95,
  epsilon,
  tau,
  logbase = 2,
  iter = 1000,
  data = NULL,
  use_future = FALSE,
  weight_fun = NULL,
  weight_params = list(epsilon = epsilon, tau = tau, logbase = logbase),
  ...
)

Arguments

Value

A list containing the rejection probabilities, posterior means, mean squared errors and mean limits of HDI intervals for all baskets as well as the family-wise error rate and the experiment-wise power.

Examples

design <- setup_fujikawa(k = 3, p0 = 0.2)

# Equal sample sizes
get_details(design = design, n = 20, p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, epsilon = 2, tau = 0, iter = 100)

# Unequal sample sizes
get_details(design = design, n = c(15, 20, 25), p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, epsilon = 2, tau = 0, iter = 100)

# A custom weight function can be defined, e.g.
weight_noshare <- function(design, n, epsilon, tau, logbase){
  n_sum <- n + 1
  return(diag(n_sum))
}
get_details(design = design, n = 20, p1 = c(0.2, 0.5, 0.5), lambda = 0.95,
           epsilon = 2, tau = 0, iter = 1000, weight_fun = weight_noshare)

Get Details of a Basket Trial Simulation with the Power Prior Design Based on Global JSD Weights

Description

Get Details of a Basket Trial Simulation with the Power Prior Design Based on Global JSD Weights

Usage

## S3 method for class 'jsdglobal'
get_details(
  design,
  n,
  p1 = NULL,
  lambda,
  level = 0.95,
  eps_pair,
  tau = 0,
  eps_all,
  logbase = 2,
  iter = 1000,
  data = NULL,
  ...
)

Arguments

Value

A list containing the rejection probabilities, posterior means, mean squared errors and mean limits of HDI intervals for all baskets as well as the family-wise error rate.

Examples

design <- setup_jsdglobal(k = 3, p0 = 0.2)
get_details(design = design, n = 20, p1 = c(0.2, 0.5, 0.5), lambda = 0.95,
  eps_pair = 2, eps_all = 2, iter = 100)

Get Details of a Basket Trial Simulation with the MML Design

Description

Get Details of a Basket Trial Simulation with the MML Design

Usage

## S3 method for class 'mml'
get_details(
  design,
  n,
  p1 = NULL,
  lambda,
  level = 0.95,
  iter = 1000,
  data = NULL,
  ...
)

Arguments

Value

A list containing the rejection probabilities, posterior means, mean squared errors and mean limits of HDI intervals for all baskets as well as the family-wise error rate.

Examples

design <- setup_mml(k = 3, p0 = 0.2)
get_details(design = design, n = 20, p1 = c(0.2, 0.5, 0.5), lambda = 0.95,
  tune_a = 1, tune_b = 1, iter = 100)

Get Details of a Basket Trial Simulation with the Global MML Design

Description

Get Details of a Basket Trial Simulation with the Global MML Design

Usage

## S3 method for class 'mmlglobal'
get_details(
  design,
  n,
  p1 = NULL,
  lambda,
  level = 0.95,
  iter = 1000,
  data = NULL,
  ...
)

Arguments

Value

A list containing the rejection probabilities, posterior means, mean squared errors and mean limits of HDI intervals for all baskets as well as the family-wise error rate.

Examples

design <- setup_mmlglobal(k = 3, p0 = 0.2)
get_details(design = design, n = 20, p1 = 0.5, lambda = 0.95, iter = 100)

Evaluate a Basket Trial

Description

Evaluate a Basket Trial

Usage

get_evaluation(design, ...)

Arguments

Value

A list containing the point estimates of the basket-specific response rates and, for some methods, the posterior probabilities that the estimated response rates are above a specified threshold p0.

Examples

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

# Equal sample sizes
get_evaluation(design = design, n = 20, r = c(10, 15, 5),
  lambda = 0.95, epsilon = 2, tau = 0, iter = 100)

# Unequal sample sizes
get_evaluation(design = design, n = c(15, 20, 25),
   r = c(10, 15, 17), lambda = 0.95, epsilon = 2,
   tau = 0, iter = 100)

Evaluate a Basket Trial with the Adaptive Power Prior Design for sequential clinical trials

Description

Evaluate a Basket Trial with the Adaptive Power Prior Design for sequential clinical trials

Usage

## S3 method for class 'app'
get_evaluation(design, n, r, lambda, level = 0.95, ...)

Arguments

Value

A list containing the point estimates of the basket-specific response rates and the posterior probabilities that the estimated response rates are above a specified threshold p0.

Examples

design <- setup_app(k = 3, p0 = 0.2)

# Equal sample sizes
get_evaluation(design = design, n = 20, r = c(10, 15, 5),
 lambda = 0.95, iter = 100)

# Unequal sample sizes
get_evaluation(design = design, n = c(15, 20, 25), r = c(10, 15, 17),
 lambda = 0.95, iter = 100)

Evaluate a BHM Basket Trial

Description

Evaluate a BHM Basket Trial

Usage

## S3 method for class 'bhm'
get_evaluation(
  design,
  n,
  r,
  lambda,
  level = 0.95,
  tau_scale,
  n_mcmc = 10000,
  ...
)

Arguments

Value

A list containing the point estimates of the basket-specific response rates.

Examples

design <- setup_bhm(k = 3, p0 = 0.2, p_target = 0.5)

get_evaluation(design = design, n = c(20, 20, 20), r = c(10, 15, 5),
  lambda = 0.95, tau_scale = 1, iter = 100)

# Unequal sample sizes
get_evaluation(design = design, n = c(15, 20, 25), r = c(10, 15, 17),
  lambda = 0.95, tau_scale = 1, iter = 100)

Evaluate a Basket Trial with the Calibrated Power Prior Design

Description

Evaluate a Basket Trial with the Calibrated Power Prior Design

Usage

## S3 method for class 'cpp'
get_evaluation(design, n, r, lambda, level = 0.95, tune_a, tune_b, ...)

Arguments

Value

A list containing the point estimates of the basket-specific response rates and the posterior probabilities that the estimated response rates are above a specified threshold p0.

Examples

design <- setup_cpp(k = 3, p0 = 0.2)

# Equal sample sizes
get_evaluation(design = design, n = 20, r = c(10, 15, 5),
  lambda = 0.95, tune_a = 1, tune_b = 1, iter = 100)

# Unequal sample sizes
get_evaluation(design = design, n = c(15, 20, 25), r = c(10, 15, 17),
  lambda = 0.95, tune_a = 1, tune_b = 1, iter = 100)

Evaluate a Basket Trial with the Limited Calibrated Power Prior Design

Description

Evaluate a Basket Trial with the Limited Calibrated Power Prior Design

Usage

## S3 method for class 'cpplim'
get_evaluation(design, n, r, lambda, level = 0.95, tune_a, tune_b, ...)

Arguments

Value

A list containing the point estimates of the basket-specific response rates and the posterior probabilities that the estimated response rates are above a specified threshold p0.

Examples

design <- setup_cpplim(k = 3, p0 = 0.2)

# Equal sample sizes
get_evaluation(design = design, n = 20, r = c(10, 15, 5),
  lambda = 0.95, tune_a = 1, tune_b = 1, iter = 100)

# Unequal sample sizes
get_evaluation(design = design, n = c(15, 20, 25), r = c(10, 15, 17),
  lambda = 0.95, tune_a = 1, tune_b = 1, iter = 100)

Evaluate a Basket Trial with the EXNEX Design

Description

Evaluate a Basket Trial with the EXNEX Design

Usage

## S3 method for class 'exnex'
get_evaluation(
  design,
  n,
  r,
  lambda,
  level = 0.95,
  tau_scale,
  w,
  n_mcmc = 10000,
  ...
)

Arguments

Value

A list containing the point estimates of the basket-specific response rates.

Examples

design <- setup_exnex(k = 3, p0 = 0.2)

# Equal sample sizes
get_evaluation(design = design, n = c(20, 20, 20), r = c(10, 15, 5),
  lambda = 0.95, tau_scale = 1, w = 0.5, iter = 100)

# Unequal sample sizes
get_evaluation(design = design, n = c(15, 20, 25), r = c(10, 15, 17),
  lambda = 0.95, tau_scale = 1, w = 0.5, iter = 100)

Evaluate a Basket Trial with Fujikawa's Design

Description

Evaluate a Basket Trial with Fujikawa's Design

Usage

## S3 method for class 'fujikawa'
get_evaluation(
  design,
  n,
  r,
  lambda,
  level = 0.95,
  epsilon,
  tau,
  logbase = 2,
  ...
)

Arguments

Value

A list containing the point estimates of the basket-specific response rates and the posterior probabilities that the estimated response rates are above a specified threshold p0.

Examples

design <- setup_fujikawa(k = 3, p0 = 0.2)

# Equal sample sizes
get_evaluation(design = design, n = 20, r = c(10, 15, 5),
  lambda = 0.95, epsilon = 2, tau = 0, iter = 100)

# Unequal sample sizes
get_evaluation(design = design, n = c(15, 20, 25),
   r = c(10, 15, 17), lambda = 0.95, epsilon = 2,
   tau = 0, iter = 100)

Get Results for Simulation of Basket Trial Designs

Description

Get Results for Simulation of Basket Trial Designs

Usage

get_results(design, ...)

Arguments

Value

A matrix of results with iter rows. A 0 means, that the null hypothesis that the response probability exceeds p0 was not rejected, a 1 means, that the null hypothesis was rejected.

Examples

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

# Equal sample sizes
get_results(design = design, n = 20, p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, epsilon = 2, tau = 0, iter = 100)

# Unequal sample sizes
get_results(design = design, n = c(15, 20, 25), p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, epsilon = 2, tau = 0, iter = 100)

Get Results for Simulation of a Basket Trial with Adaptive Power Prior Design

Description

Get Results for Simulation of a Basket Trial with Adaptive Power Prior Design

Usage

## S3 method for class 'app'
get_results(design, n, p1 = NULL, lambda, iter = 1000, data = NULL, ...)

Arguments

Value

A matrix of results with iter rows. A 0 means, that the null hypothesis that the response probability exceeds p0 was not rejected, a 1 means, that the null hypothesis was rejected.

Examples

design <- setup_app(k = 3, p0 = 0.2)

# Equal sample sizes
get_results(design = design, n = 20, p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, iter = 100)

# Unequal sample sizes
get_results(design = design, n = c(15, 20, 25), p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, iter = 100)

Get Results for Simulation of a Basket Trial with the BHM Design

Description

Get Results for Simulation of a Basket Trial with the BHM Design

Usage

## S3 method for class 'bhm'
get_results(
  design,
  n,
  p1 = NULL,
  lambda,
  tau_scale,
  iter = 1000,
  n_mcmc = 10000,
  data = NULL,
  ...
)

Arguments

Value

A matrix of results with iter rows. A 0 means, that the null hypothesis that the response probability exceeds p0 was not rejected, a 1 means, that the null hypothesis was rejected.

Examples

design <- setup_bhm(k = 3, p0 = 0.2, p_target = 0.5)

# Equal sample sizes
get_results(design, n = 20, p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, tau_scale = 1, iter = 100)

# Unequal sample sizes
get_results(design, n = c(15, 20, 25), p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, tau_scale = 1, iter = 100)

Get Results for Simulation of a Basket Trial with a Calibrated Power Prior Design

Description

Get Results for Simulation of a Basket Trial with a Calibrated Power Prior Design

Usage

## S3 method for class 'cpp'
get_results(
  design,
  n,
  p1 = NULL,
  lambda,
  tune_a,
  tune_b,
  iter = 1000,
  data = NULL,
  ...
)

Arguments

Value

A matrix of results with iter rows. A 0 means, that the null hypothesis that the response probability exceeds p0 was not rejected, a 1 means, that the null hypothesis was rejected.

Examples

design <- setup_cpp(k = 3, p0 = 0.2)

# Equal sample sizes
get_results(design = design, n = 20, p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, tune_a = 1, tune_b = 1, iter = 100)

# Unequal sample sizes
get_results(design = design, n = c(15, 20, 25), p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, tune_a = 1, tune_b = 1, iter = 100)

Get Results for Simulation of a Basket Trial with a Global Calibrated Power Prior Design

Description

Get Results for Simulation of a Basket Trial with a Global Calibrated Power Prior Design

Usage

## S3 method for class 'cppglobal'
get_results(
  design,
  n,
  p1 = NULL,
  lambda,
  tune_a,
  tune_b,
  epsilon,
  iter = 1000,
  data = NULL,
  ...
)

Arguments

Value

A matrix of results with iter rows. A 0 means, that the null hypothesis that the response probability exceeds p0 was not rejected, a 1 means, that the null hypothesis was rejected.

Examples

design <- setup_cppglobal(k = 3, p0 = 0.2)
get_results(design = design, n = 20, p1 = c(0.2, 0.5, 0.5), lambda = 0.95,
  tune_a = 1, tune_b = 1, epsilon = 2, iter = 100)

Get Results for Simulation of a Basket Trial with a Limited Calibrated Power Prior Design

Description

Get Results for Simulation of a Basket Trial with a Limited Calibrated Power Prior Design

Usage

## S3 method for class 'cpplim'
get_results(
  design,
  n,
  p1 = NULL,
  lambda,
  tune_a,
  tune_b,
  iter = 1000,
  data = NULL,
  ...
)

Arguments

Value

A matrix of results with iter rows. A 0 means, that the null hypothesis that the response probability exceeds p0 was not rejected, a 1 means, that the null hypothesis was rejected.

Examples

design <- setup_cpplim(k = 3, p0 = 0.2)

# Equal sample sizes
get_results(design = design, n = 20, p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, tune_a = 1, tune_b = 1, iter = 100)

# Unequal sample sizes
get_results(design = design, n = c(15, 20, 25), p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, tune_a = 1, tune_b = 1, iter = 100)

Get Results for Simulation of a Basket Trial with the EXNEX Design

Description

Get Results for Simulation of a Basket Trial with the EXNEX Design

Usage

## S3 method for class 'exnex'
get_results(
  design,
  n,
  p1 = NULL,
  lambda,
  tau_scale,
  w,
  iter = 1000,
  n_mcmc = 10000,
  data = NULL,
  ...
)

Arguments

Value

A matrix of results with iter rows. A 0 means, that the null hypothesis that the response probability exceeds p0 was not rejected, a 1 means, that the null hypothesis was rejected.

Examples

design <- setup_exnex(k = 3, p0 = 0.2)

# Equal sample sizes
get_results(design, n = 20, p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, tau_scale = 1, w = 0.5, iter = 100)

# Unequal sample sizes
get_results(design, n = c(15, 20, 25), p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, tau_scale = 1, w = 0.5, iter = 100)

Get Results for Simulation of a Basket Trial with Fujikawa's Design

Description

Get Results for Simulation of a Basket Trial with Fujikawa's Design

Usage

## S3 method for class 'fujikawa'
get_results(
  design,
  n,
  p1 = NULL,
  lambda,
  epsilon,
  tau,
  logbase = 2,
  iter = 1000,
  data = NULL,
  ...
)

Arguments

Value

A matrix of results with iter rows. A 0 means, that the null hypothesis that the response probability exceeds p0 was not rejected, a 1 means, that the null hypothesis was rejected.

Examples

design <- setup_fujikawa(k = 3, p0 = 0.2)

# Equal sample sizes
get_results(design = design, n = 20, p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, epsilon = 2, tau = 0, iter = 100)

# Unequal sample sizes
get_results(design = design, n = c(15, 20, 25), p1 = c(0.2, 0.5, 0.5),
  lambda = 0.95, epsilon = 2, tau = 0, iter = 100)

Get Results for Simulation of a Basket Trial with the Power Prior Design Based on Global JSD Weights

Description

Get Results for Simulation of a Basket Trial with the Power Prior Design Based on Global JSD Weights

Usage

## S3 method for class 'jsdglobal'
get_results(
  design,
  n,
  p1 = NULL,
  lambda,
  eps_pair,
  tau = 0,
  eps_all,
  logbase = 2,
  iter = 1000,
  data = NULL,
  ...
)

Arguments

Value

A matrix of results with iter rows. A 0 means, that the null hypothesis that the response probability exceeds p0 was not rejected, a 1 means, that the null hypothesis was rejected.

Examples

design <- setup_jsdglobal(k = 3, p0 = 0.2)
get_results(design = design, n = 20, p1 = c(0.2, 0.5, 0.5), lambda = 0.95,
  eps_pair = 2, eps_all = 2, iter = 100)

Get Results for Simulation of a Basket Trial with the MML Design

Description

Get Results for Simulation of a Basket Trial with the MML Design

Usage

## S3 method for class 'mml'
get_results(design, n, p1 = NULL, lambda, iter = 1000, data = NULL, ...)

Arguments

Value

A matrix of results with iter rows. A 0 means, that the null hypothesis that the response probability exceeds p0 was not rejected, a 1 means, that the null hypothesis was rejected.

Examples

design <- setup_mml(k = 3, p0 = 0.2)
get_results(design, n = 20, p1 = c(0.2, 0.5, 0.5), lambda = 0.95,
  iter = 100)

Get Results for Simulation of a Basket Trial with the Global MML Design

Description

Get Results for Simulation of a Basket Trial with the Global MML Design

Usage

## S3 method for class 'mmlglobal'
get_results(design, n, p1 = NULL, lambda, iter = 1000, data = NULL, ...)

Arguments

Value

A matrix of results with iter rows. A 0 means, that the null hypothesis that the response probability exceeds p0 was not rejected, a 1 means, that the null hypothesis was rejected.

Examples

design <- setup_mmlglobal(k = 3, p0 = 0.2)
get_results(design, n = 20, p1 = c(0.2, 0.5, 0.5), lambda = 0.95,
  iter = 100)

Create a Scenario Matrix

Description

Creates a default scenario matrix.

Usage

get_scenarios(design, p1)

Arguments

Details

get_scenarios creates a default scenario matrix that can be used for opt_design. The function creates k + 1 scenarios, from a global null to a global alternative scenario.

Value

A matrix with k rows and k + 1 columns.

Examples

design <- setup_fujikawa(k = 3, p0 = 0.2)
get_scenarios(design = design, p1 = 0.5)

Optimize a Basket Trial Design

Description

Optimize a Basket Trial Design

Usage

opt_design(
  design,
  n,
  alpha,
  design_params = list(),
  scenarios,
  prec_digits,
  iter = 1000,
  data = NULL,
  ...
)

Arguments

Value

A matrix with the expected number of correct decisions.

Examples

design <- setup_fujikawa(k = 3, p0 = 0.2)
scenarios <- get_scenarios(design, p1 = 0.5)

## Equal sample sizes
# Without simulated data

opt_design(design, n = 20, alpha = 0.05, design_params =
           list(epsilon = c(1, 2), tau = c(0, 0.5)), scenarios = scenarios,
           prec_digits = 3)


# With simulated data
scenario_list <- as.list(data.frame(scenarios))
data_list <- lapply(scenario_list,
  function(x) get_data(k = 3, n = 20, p = x, iter = 1000))

opt_design(design, n = 20, alpha = 0.05, design_params =
           list(epsilon = c(1, 2), tau = c(0, 0.5)), scenarios = scenarios,
           prec_digits = 3, data = data_list)


## Unequal sample sizes
# Without simulated data
opt_design(design, n = c(15, 20, 25), alpha = 0.05, design_params =
           list(epsilon = c(1, 2), tau = c(0, 0.5)), scenarios = scenarios,
           prec_digits = 3)

# With simulated data
scenario_list <- as.list(data.frame(scenarios))
data_list <- lapply(scenario_list,
                    function(x) get_data(k = 3, n = c(15, 20, 25),
                                         p = x, iter = 1000))
opt_design(design, n = c(15, 20, 25), alpha = 0.05, design_params =
           list(epsilon = c(1, 2), tau = c(0, 0.5)), scenarios = scenarios,
           prec_digits = 3, data = data_list)

Setup Adaptive Power Prior Design Object

Description

Setup Adaptive Power Prior Design Object

Usage

setup_app(k, p0, shape1 = 1, shape2 = 1)

Arguments

Details

The class app implements the adaptive power prior design for sequential clinical trials proposed by Ollier et al. (2020).

Value

An S3 object of class app

References

Ollier, A., Morita, S., Ursino, M., & Zohar, S. (2020). An adaptive power prior for sequential clinical trials - Application to bridging studies. Statistical methods in medical research, 29(8), 2282–2294.

Examples

design_app <- setup_app(k = 3, p0 = 0.2)

Setup BHM Design Object

Description

Setup BHM Design Object

Usage

setup_bhm(k, p0, p_target, mu_mean = NULL, mu_sd = 100)

Arguments

Details

The class bhm implements the Bayesian Hierarchical Model proposed by Berry et al. (2013). Methods for this class are mostly wrappers for functions from the package bhmbasket.

In the BHM the thetas of all baskets are modeled, where theta_i = logit(p_i) - logit(p_target). These thetas are assumed to come from a normal distribution with mean mu_mean and standard deviation mu_sd. If mu_mean = NULL then mu_mean is determined as logit(p0) - logit(p_target), hence the mean of the normal distribution corresponds to the null hypothesis.

Value

An S3 object of class bhm

References

Berry, S. M., Broglio, K. R., Groshen, S., & Berry, D. A. (2013). Bayesian hierarchical modeling of patient subpopulations: efficient designs of phase II oncology clinical trials. Clinical Trials, 10(5), 720-734.

Examples

design_bhm <- setup_bhm(k = 3, p0 = 0.2, p_target = 0.5)

Setup Calibrated Power Prior Design Object

Description

Setup Calibrated Power Prior Design Object

Usage

setup_cpp(k, p0, shape1 = 1, shape2 = 1)

Arguments

Details

The class cpp implements a version of the power prior design, in which the amount of information that is shared between baskets is determined by the Kolmogorov-Smirnov test statistic between baskets (which is equivalent to the absolut difference in response rates).

Value

An S3 object of class cpp

References

Baumann, L., Sauer, L. D., & Kieser, M. (2025). A Basket Trial Design Based on Power Priors. Statistics in Biopharmaceutical Research, 17(3), 446–456. https://doi.org/10.1080/19466315.2024.2402275

Examples

design_cpp <- setup_cpp(k = 3, p0 = 0.2)

Setup Global Calibrated Power Prior Design Object

Description

Setup Global Calibrated Power Prior Design Object

Usage

setup_cppglobal(k, p0, shape1 = 1, shape2 = 1)

Arguments

Details

The class cppglobal implements a version of the power prior design, in which the amount of information that is shared between baskets is determined by the Kolmogorov-Smirnov test statistic between basekts and a function based on response rate differences that quantifies the overall heterogeneity.

Value

An S3 object of class cppglobal

References

Baumann, L., Sauer, L. D., & Kieser, M. (2025). A Basket Trial Design Based on Power Priors. Statistics in Biopharmaceutical Research, 17(3), 446–456. https://doi.org/10.1080/19466315.2024.2402275

Examples

design_cppglobal <- setup_cppglobal(k = 3, p0 = 0.2)

Setup Limited Calibrated Power Prior Design Object

Description

Setup Limited Calibrated Power Prior Design Object

Usage

setup_cpplim(k, p0, shape1 = 1, shape2 = 1)

Arguments

Details

The class cpplim implements a combined version of the adaptive power prior (app) and the calibrated power prior (cpp), where the parameter limiting the amount of information to be borrowed in the adaptive power prior design is included in the calibrated power prior design.

Value

An S3 object of class cpplim

References

Ollier, A., Morita, S., Ursino, M., & Zohar, S. (2020). An adaptive power prior for sequential clinical trials - Application to bridging studies. Statistical methods in medical research, 29(8), 2282–2294.

Baumann, L., Sauer, L. D., & Kieser, M. (2025). A Basket Trial Design Based on Power Priors. Statistics in Biopharmaceutical Research, 17(3), 446–456. https://doi.org/10.1080/19466315.2024.2402275

Examples

design_cpplim <- setup_cpplim(k = 3, p0 = 0.2)

Setup EXNEX Design Object

Description

Setup EXNEX Design Object

Usage

setup_exnex(
  k,
  p0,
  basket_mean = NULL,
  basket_sd = 100,
  mu_mean = NULL,
  mu_sd = 100
)

Arguments

Details

The class exnex implements the EXNEX model proposed by Neuenschwander et al. (2016). Methods for this class are mostly wrappers for functions from the package bhmbasket.

In the EXNEX model the thetas of all baskets are modeled as a mixture of individual models and a Bayesian Hierarchical Model with a fixed mixture weight w. If mu_mean and basket_mean are NULL then they are set to logit(p0). Note that Neuenschwander et al. (2016) use different prior means and standard deviations. The default values here are used for better comparison with the BHM model (see setup_bhm).

Value

An S3 object of class exnex

References

Neuenschwander, B., Wandel, S., Roychoudhury, S., & Bailey, S. (2016). Robust exchangeability designs for early phase clinical trials with multiple strata. Pharmaceutical statistics, 15(2), 123-134.

Examples

design_exnex <- setup_exnex(k = 3, p0 = 0.2)

Setup Fujikawa Design Object

Description

Setup Fujikawa Design Object

Usage

setup_fujikawa(k, p0, shape1 = 1, shape2 = 1)

Arguments

Details

The class fujikawa implements a design by Fujikawa et al. (2020) in which information is shared based on the pairwise similarity between baskets which is quantified using the Jensen-Shannon divergence between the individual posterior distributions between baskets.

Value

An S3 object of class fujikawa

References

Fujikawa, K., Teramukai, S., Yokota, I., & Daimon, T. (2020). A Bayesian basket trial design that borrows information across strata based on the similarity between the posterior distributions of the response probability. Biometrical Journal, 62(2), 330-338.

Examples

design_fujikawa <- setup_fujikawa(k = 3, p0 = 0.2)

Setup Global JSD Design Object

Description

Setup Global JSD Design Object

Usage

setup_jsdglobal(k, p0, shape1 = 1, shape2 = 1)

Arguments

Details

The class jsdglobal implements a version of the power prior design, in which information is shared based on pairwise similarity and overall heterogeneity between baskets. Both pairwise similarity and overall heterogeneity are assessed using the Jensen-Shannon divergence.

Value

An S3 object of class jsdglobal

References

Baumann, L., Sauer, L. D., & Kieser, M. (2025). A Basket Trial Design Based on Power Priors. Statistics in Biopharmaceutical Research, 17(3), 446–456. https://doi.org/10.1080/19466315.2024.2402275

Examples

design_jsdglobal <- setup_jsdglobal(k = 3, p0 = 0.2)

Setup mml Design Object

Description

Creates an object of class mml.

Usage

setup_mml(k, p0, shape1 = 1, shape2 = 1)

Arguments

Details

The class mml implements a modified version of the empirical Bayes method by Gravestock & Held (2017) which was proposed for borrowing strength from an external study. In their approach, the sharing weight is found as the maximum of the marginal likelihood of the weight, given the external data set. This leads, however, to non-symmetric weights when applied to sharing in basket trials, i.e. Basket i would not share the information from Basket j as the other way round. Therefore, a symmetrised version is used, where the mean of the two weights resulting from sharing in both directions is used.

Value

An S3 object of class mml

References

Gravestock, I., & Held, L. (2017). Adaptive power priors with empirical Bayes for clinical trials. Pharmaceutical statistics, 16(5), 349-360.

Examples

design_mml <- setup_mml(k = 3, p0 = 0.2)

Setup mmlglobal Design Object

Description

Creates an object of class mmlglobal.

Usage

setup_mmlglobal(k, p0, shape1 = 1, shape2 = 1)

Arguments

Details

The class mmlglobal implements an empirical Bayes method by Gravestock & Held (2019) which was proposed for borrowing strength from multiple external studies.

Value

An S3 object of class mmlglobal

References

Gravestock, I., & Held, L. (2019). Power priors based on multiple historical studies for binary outcomes. Biometrical Journal, 61(5), 1201-1218.

Baumann, L., Sauer, L. D., & Kieser, M. (2025). A Basket Trial Design Based on Power Priors. Statistics in Biopharmaceutical Research, 17(3), 446–456. https://doi.org/10.1080/19466315.2024.2402275

Examples

design_mmlglobal <- setup_mmlglobal(k = 3, p0 = 0.2)

Calculate the Type 1 Error Rate for a Basket Trial Design

Description

Calculate the Type 1 Error Rate for a Basket Trial Design

Usage

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

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
toer(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
toer(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)