Package 'bhmbasket' reference manual

Title:	Bayesian Hierarchical Models for Basket Trials
Description:	Provides functions for the evaluation of basket trial designs with binary endpoints. Operating characteristics of a basket trial design are assessed by simulating trial data according to scenarios, analyzing the data with Bayesian hierarchical models (BHMs), and assessing decision probabilities on stratum and trial-level based on Go / No-go decision making. The package is build for high flexibility regarding decision rules, number of interim analyses, number of strata, and recruitment. The BHMs proposed by Berry et al. (2013) <doi:10.1177/1740774513497539> and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>, as well as a model that combines both approaches are implemented. Functions are provided to implement Bayesian decision rules as for example proposed by Fisch et al. (2015) <doi:10.1177/2168479014533970>. In addition, posterior point estimates (mean/median) and credible intervals for response rates and some model parameters can be calculated. For simulated trial data, bias and mean squared errors of posterior point estimates for response rates can be provided.
Authors:	Stephan Wojciekowski [aut, cre]
Maintainer:	Stephan Wojciekowski <[email protected]>
License:	GPL-3
Version:	0.9.5
Built:	2024-11-01 04:54:57 UTC
Source:	https://github.com/cran/bhmbasket

combinePriorParameters

Description

This function combines prior parameters from different sources and returns them for use in performAnalyses.

Usage

combinePriorParameters(list_of_prior_parameters)
combinePriorParameters(list_of_prior_parameters)

Arguments

list_of_prior_parameters

A list of items with class prior_parameters_list

Details

This function is intended to combine the prior parameters set with the functions setPriorParametersBerry, setPriorParametersExNex, setPriorParametersExNexAdj, setPriorParametersPooled, or setPriorParametersStratified, in case more than one analysis method should be applied with performAnalyses.

Value

A list with prior parameters of class prior_parameters_list

Author(s)

Stephan Wojciekowski

Examples

 prior_parameters_stratified <- setPriorParametersStratified(c(1, 2), c(3, 4))
 prior_parameters_berry      <- setPriorParametersBerry(0, 1, 2)

 prior_parameters_list       <- combinePriorParameters(
   list(prior_parameters_berry,
        prior_parameters_stratified))
prior_parameters_stratified <- setPriorParametersStratified(c(1, 2), c(3, 4))
 prior_parameters_berry      <- setPriorParametersBerry(0, 1, 2)

 prior_parameters_list       <- combinePriorParameters(
   list(prior_parameters_berry,
        prior_parameters_stratified))

continueRecruitment

Description

This function continues the recruitment of subjects for a set of scenarios based on the Go / NoGo decisions in the simulated trial outcomes of said scenarios.

Usage

continueRecruitment(n_subjects_add_list, decisions_list, method_name)
continueRecruitment(n_subjects_add_list, decisions_list, method_name)

Arguments

`n_subjects_add_list`	A list that contains for each scenario an integer vector for the number of subjects per cohort to be additionally recruited.
`decisions_list`	A list with decisions per scenario created with `getGoDecisions`
`method_name`	A string for the method name of the analysis the decisions are based on

Details

This function is intended to be used for analyses with the following work flow:
simulateScenarios() -> performAnalyses() -> getGoDecisions()->
continueRecruitment() -> performAnalyses() -> getGoDecisions()->
continueRecruitment() -> ...

Note that n_subjects_add_list takes the additional number of subjects to be recruited, not the overall number of subjects. This way the work flow can be repeated as often as required, which can be useful e.g. for interim analyses.

Value

An object of class scenario_list with the scenario data for each specified scenario.

Author(s)

Stephan Wojciekowski

Examples

interim_scenarios <- simulateScenarios(
  n_subjects_list     = list(c(10, 20, 30)),
  response_rates_list = list(rep(0.9, 3)),
  n_trials            = 10)

interim_analyses <- performAnalyses(
  scenario_list       = interim_scenarios,
  target_rates        = rep(0.5, 3),
  n_mcmc_iterations   = 100)

interim_gos <- getGoDecisions(
  analyses_list       = interim_analyses,
  cohort_names        = c("p_1", "p_2", "p_3"),
  evidence_levels     = c(0.5, 0.8, 0.5),
  boundary_rules      = quote(c(x[1] > 0.8, x[2] > 0.6, x[3] > 0.7)))

scenarios_list <- continueRecruitment(
  n_subjects_add_list = list(c(30, 20, 10)),
  decisions_list      = interim_gos,
  method_name         = "exnex_adj")
interim_scenarios <- simulateScenarios(
  n_subjects_list     = list(c(10, 20, 30)),
  response_rates_list = list(rep(0.9, 3)),
  n_trials            = 10)

interim_analyses <- performAnalyses(
  scenario_list       = interim_scenarios,
  target_rates        = rep(0.5, 3),
  n_mcmc_iterations   = 100)

interim_gos <- getGoDecisions(
  analyses_list       = interim_analyses,
  cohort_names        = c("p_1", "p_2", "p_3"),
  evidence_levels     = c(0.5, 0.8, 0.5),
  boundary_rules      = quote(c(x[1] > 0.8, x[2] > 0.6, x[3] > 0.7)))

scenarios_list <- continueRecruitment(
  n_subjects_add_list = list(c(30, 20, 10)),
  decisions_list      = interim_gos,
  method_name         = "exnex_adj")

createTrial

Description

This function creates an object of class scenario_list for a single trial outcome, which can subsequently be analyzed with other functions of bhmbasket, e.g. performAnalyses

Usage

createTrial(n_subjects, n_responders)
createTrial(n_subjects, n_responders)

Arguments

`n_subjects`	A vector of integers for the number of subjects in the trial outcome
`n_responders`	A vector of integers for the number of responders in the trial outcome

Details

This function is a wrapper for simulateScenarios with

simulateScenarios(
 n_subjects_list     = list(n_subjects),
 response_rates_list = list(n_responders),
 n_trials            = 1)

Value

An object of class scenario_list with the scenario data for a single trial outcome.

Author(s)

Stephan Wojciekowski

Examples

 trial_outcome <- createTrial(n_subjects   = c(10, 20, 30, 40),
                              n_responders = c( 1,  2,  3,  4))
trial_outcome <- createTrial(n_subjects   = c(10, 20, 30, 40),
                              n_responders = c( 1,  2,  3,  4))

getEstimates

Description

This function calculates the point estimates and credible intervals per cohort, as well as estimates of the biases and the mean squared errors of the point estimates per cohort

Usage

getEstimates(
  analyses_list,
  add_parameters = NULL,
  point_estimator = "median",
  alpha_level = 0.05
)
getEstimates(
  analyses_list,
  add_parameters = NULL,
  point_estimator = "median",
  alpha_level = 0.05
)

Arguments

`analyses_list`	An object of class `analysis_list`, as created with `performAnalyses`
`add_parameters`	A vector of strings naming additional parameters from the Bayesian hierarchical models, e.g. `c('mu', 'tau')`. If `NULL`, no additional parameters will be evaluated, Default: `NULL`
`point_estimator`	A string indicating the type of estimator used for calculation of bias and MSE. Must be one of `'median'` or `'mean'`
`alpha_level`	A numeric in (0, 1) for the level of the credible interval. Only values corresponding to quantiles saved in `performAnalyses` will work, Default: `0.05`

Details

Bias and MSE will only be calculated for response rate estimates of simulated trials. For additional parameters, bias and MSE will not be calculated.

Possible additional parameters are for the Bayesian hierarchical models are c('mu', 'tau') for 'berry', 'exnex', and 'exnex_adj'. The latter two models can also access the posterior weights paste0("w_", seq_len(n_cohorts)).

Value

A named list of matrices of estimates of response rates and credible intervals. Estimates of bias and MSE are included for response rate estimates of simulated trials.

Author(s)

Stephan Wojciekowski

Examples

  scenarios_list <- simulateScenarios(
    n_subjects_list     = list(c(10, 20, 30)),
    response_rates_list = list(c(0.1, 0.2, 3)),
    n_trials            = 10)

  analyses_list <- performAnalyses(
    scenario_list       = scenarios_list,
    target_rates        = c(0.1, 0.1, 0.1),
    calc_differences    = matrix(c(3, 2, 2, 1), ncol = 2),
    n_mcmc_iterations   = 100)

  getEstimates(analyses_list)
  getEstimates(analyses_list   = analyses_list,
               add_parameters  = c("mu", "tau", "w_1", "w_2", "w_3"),
               point_estimator = "mean",
               alpha_level     = 0.1)

  outcome <- createTrial(
    n_subjects          = c(10, 20, 30),
    n_responders        = c( 1,  2,  3))

  outcome_analysis <- performAnalyses(
    scenario_list       = outcome,
    target_rates        = c(0.1, 0.1, 0.1),
    n_mcmc_iterations   = 100)

  getEstimates(outcome_analysis)
  getEstimates(analyses_list  = outcome_analysis,
               add_parameters = c("mu", "w_1", "w_2", "w_3"))
scenarios_list <- simulateScenarios(
    n_subjects_list     = list(c(10, 20, 30)),
    response_rates_list = list(c(0.1, 0.2, 3)),
    n_trials            = 10)

  analyses_list <- performAnalyses(
    scenario_list       = scenarios_list,
    target_rates        = c(0.1, 0.1, 0.1),
    calc_differences    = matrix(c(3, 2, 2, 1), ncol = 2),
    n_mcmc_iterations   = 100)

  getEstimates(analyses_list)
  getEstimates(analyses_list   = analyses_list,
               add_parameters  = c("mu", "tau", "w_1", "w_2", "w_3"),
               point_estimator = "mean",
               alpha_level     = 0.1)

  outcome <- createTrial(
    n_subjects          = c(10, 20, 30),
    n_responders        = c( 1,  2,  3))

  outcome_analysis <- performAnalyses(
    scenario_list       = outcome,
    target_rates        = c(0.1, 0.1, 0.1),
    n_mcmc_iterations   = 100)

  getEstimates(outcome_analysis)
  getEstimates(analyses_list  = outcome_analysis,
               add_parameters = c("mu", "w_1", "w_2", "w_3"))

getGoDecisions

Description

This function applies decision rules to the analyzed trials. The resulting decision_list can be further processed with getGoProbabilities or continueRecruitment.

Usage

getGoDecisions(
  analyses_list,
  cohort_names,
  evidence_levels,
  boundary_rules,
  overall_min_gos = 1
)
getGoDecisions(
  analyses_list,
  cohort_names,
  evidence_levels,
  boundary_rules,
  overall_min_gos = 1
)

Arguments

`analyses_list`	An object of class `analysis_list`, as created with `performAnalyses`
`cohort_names`	A vector of strings with the names of the cohorts, e.g. `c('p_1', 'p_2')`
`evidence_levels`	A vector of numerics in `(0, 1)` for the posterior probability thresholds for the cohorts. Will be recycled to match the number of methods in the `analyses_list`
`boundary_rules`	A quote of a vector for the boundary rules, `quote(c(...))`, see details. The number of decisions to be taken must match the number of cohorts. Will be recycled to match the number of methods in the `analyses_list`
`overall_min_gos`	A positive integer for the minimum number of cohort-wise go decisions required for an overall go decision Default: `1`

Details

This function applies decision rules of the following type to the outcomes of (simulated) basket trials with binary endpoints:

$P(p_j|data > p_{B,j}) > \gamma,$

where $p_j|data$ is the posterior response rate of cohort $j$ , $p_{B,j}$ is the response rate boundary of cohort $j$ , and $\gamma$ is the evidence level. This rule can equivalently be written as

$q_{1-\gamma,j} > p_{B,j},$

where $q_{1-\gamma,j}$ is the $1-\gamma$ -quantile of the posterior response rate of cohort $j$ .

The arguments cohort_names and evidence_levels determine $q_{1-\gamma,j}$ , where the entries of cohort_names and evidence_levels are matched corresponding to their order.

The argument boundary_rules provides the rules that describe what should happen with the posterior quantiles $q_{1-\gamma,j}$ . The first posterior quantile determined by the first items of cohort_names and evidence_levels is referred to as x[1], the second as x[2], etc. Using the quote(c(...))-notation, many different rules can be implemented. A decision rule for only one cohort would be boundary_rules = quote(c(x[1] > 0.1)), cohort_names = 'p_1', and evidence_levels = 0.5, which implements the rule $P(p_1|data > 0.1) > 0.5$ . The number of decisions to be taken must match the number of cohorts, i.e. for each cohort there must be a decision rule in the vector separated by a comma. See the example section for a decision rule for more than one cohort and the example of negateGoDecisions for the implementation of a more complex decision rule.

Value

An object of class decision_list

Author(s)

Stephan Wojciekowski

Examples

scenarios_list <- simulateScenarios(
  n_subjects_list     = list(c(10, 20, 30)),
  response_rates_list = list(c(0.1, 0.1, 0.9)),
  n_trials            = 10)

analyses_list <- performAnalyses(
  scenario_list      = scenarios_list,
  target_rates       = rep(0.5, 3),
  n_mcmc_iterations  = 100)

## Decision rule for more than one cohort
decisions_list <- getGoDecisions(
  analyses_list   = analyses_list,
  cohort_names    = c("p_1", "p_2", "p_3"),
  evidence_levels = c(0.5, 0.5, 0.8),
  boundary_rules  = quote(c(x[1] > 0.7, x[2] < 0.3, x[3] < 0.6)))

## Decision rule for only two of the three cohorts
decisions_list <- getGoDecisions(
  analyses_list   = analyses_list,
  cohort_names    = c("p_1", "p_3"),
  evidence_levels = c(0.5, 0.8),
  boundary_rules  = quote(c(x[1] > 0.7, TRUE, x[3] < 0.6)),
  overall_min_gos = 2L)

## Different decision rules for each method
## This works the same way for the different evidence_levels
decisions_list <- getGoDecisions(
  analyses_list   = analyses_list,
  cohort_names    = c("p_1", "p_2", "p_3"),
  evidence_levels = c(0.5, 0.5, 0.8),
  boundary_rules  = list(quote(c(x[1] > 0.1, x[2] < 0.5, x[3] < 0.1)),  # "berry"
                         quote(c(x[1] > 0.2, x[2] < 0.4, x[3] < 0.2)),  # "exnex"
                         quote(c(x[1] > 0.3, x[2] < 0.3, x[3] < 0.3)),  # "exnex_adj"
                         quote(c(x[1] > 0.4, x[2] < 0.2, x[3] < 0.4)),  # "pooled"
                         quote(c(x[1] > 0.5, x[2] < 0.1, x[3] < 0.5)))) # "stratified"
scenarios_list <- simulateScenarios(
  n_subjects_list     = list(c(10, 20, 30)),
  response_rates_list = list(c(0.1, 0.1, 0.9)),
  n_trials            = 10)

analyses_list <- performAnalyses(
  scenario_list      = scenarios_list,
  target_rates       = rep(0.5, 3),
  n_mcmc_iterations  = 100)

## Decision rule for more than one cohort
decisions_list <- getGoDecisions(
  analyses_list   = analyses_list,
  cohort_names    = c("p_1", "p_2", "p_3"),
  evidence_levels = c(0.5, 0.5, 0.8),
  boundary_rules  = quote(c(x[1] > 0.7, x[2] < 0.3, x[3] < 0.6)))

## Decision rule for only two of the three cohorts
decisions_list <- getGoDecisions(
  analyses_list   = analyses_list,
  cohort_names    = c("p_1", "p_3"),
  evidence_levels = c(0.5, 0.8),
  boundary_rules  = quote(c(x[1] > 0.7, TRUE, x[3] < 0.6)),
  overall_min_gos = 2L)

## Different decision rules for each method
## This works the same way for the different evidence_levels
decisions_list <- getGoDecisions(
  analyses_list   = analyses_list,
  cohort_names    = c("p_1", "p_2", "p_3"),
  evidence_levels = c(0.5, 0.5, 0.8),
  boundary_rules  = list(quote(c(x[1] > 0.1, x[2] < 0.5, x[3] < 0.1)),  # "berry"
                         quote(c(x[1] > 0.2, x[2] < 0.4, x[3] < 0.2)),  # "exnex"
                         quote(c(x[1] > 0.3, x[2] < 0.3, x[3] < 0.3)),  # "exnex_adj"
                         quote(c(x[1] > 0.4, x[2] < 0.2, x[3] < 0.4)),  # "pooled"
                         quote(c(x[1] > 0.5, x[2] < 0.1, x[3] < 0.5)))) # "stratified"

getGoProbabilities

Description

Calculates the Go probabilities for given decisions

Usage

getGoProbabilities(go_decisions_list, nogo_decisions_list = NULL)
getGoProbabilities(go_decisions_list, nogo_decisions_list = NULL)

Arguments

`go_decisions_list`	An object of class `decision_list`, as returned by `getGoDecisions`
`nogo_decisions_list`	An object of class `decision_list`, as returned by `getGoDecisions`, Default: `NULL`

Details

If only go_decisions_list is provided (i.e. nogo_decisions_list is NULL), only Go probabilities will be calculated. If both go_decisions_list and nogo_decisions_list are provided, Go, Consider, and NoGo probabilities will be calculated.

Value

A list of matrices of Go (and Consider and NoGo) probabilities

Author(s)

Stephan Wojciekowski

Examples

scenarios_list <- simulateScenarios(
  n_subjects_list     = list(c(10, 20)),
  response_rates_list = list(rep(0.9, 2)),
  n_trials            = 10)

analyses_list <- performAnalyses(
  scenario_list       = scenarios_list,
  target_rates        = rep(0.5, 2),
  n_mcmc_iterations   = 100)

go_decisions_list <- getGoDecisions(
  analyses_list       = analyses_list,
  cohort_names        = c("p_1", "p_2"),
  evidence_levels     = c(0.5, 0.8),
  boundary_rules      = quote(c(x[1] > 0.8, x[2] > 0.6)))

nogo_decisions_list <- getGoDecisions(
  analyses_list       = analyses_list,
  cohort_names        = c("p_1", "p_2"),
  evidence_levels     = c(0.5, 0.8),
  boundary_rules      = quote(c(x[1] < 0.5, x[2] < 0.3)))

getGoProbabilities(go_decisions_list)
getGoProbabilities(go_decisions_list, nogo_decisions_list)
scenarios_list <- simulateScenarios(
  n_subjects_list     = list(c(10, 20)),
  response_rates_list = list(rep(0.9, 2)),
  n_trials            = 10)

analyses_list <- performAnalyses(
  scenario_list       = scenarios_list,
  target_rates        = rep(0.5, 2),
  n_mcmc_iterations   = 100)

go_decisions_list <- getGoDecisions(
  analyses_list       = analyses_list,
  cohort_names        = c("p_1", "p_2"),
  evidence_levels     = c(0.5, 0.8),
  boundary_rules      = quote(c(x[1] > 0.8, x[2] > 0.6)))

nogo_decisions_list <- getGoDecisions(
  analyses_list       = analyses_list,
  cohort_names        = c("p_1", "p_2"),
  evidence_levels     = c(0.5, 0.8),
  boundary_rules      = quote(c(x[1] < 0.5, x[2] < 0.3)))

getGoProbabilities(go_decisions_list)
getGoProbabilities(go_decisions_list, nogo_decisions_list)

getMuVar

Description

This function returns the variance of $\mu$ that is worth a certain number of subjects for the distribution of the response rates.

Usage

getMuVar(response_rate, tau_scale, n_worth = 1)
getMuVar(response_rate, tau_scale, n_worth = 1)

Arguments

`response_rate`	A numeric for the response rate
`tau_scale`	A numeric for the scale parameter of the Half-normal distribution of $\tau$
`n_worth`	An integer for the number of subjects the variance of $\mu$ should be worth with regard to the variability of the distribution of the response rate, Default: `1`

Details

Calculates the variance mu_var in

$logit(p) = \theta ~ N(\mu, \tau), \mu ~ N(mu_mean, mu_var), \tau ~ HN(tau_scale),$

for n_worth number of observations, as in Neuenschwander et al. (2016).

Value

Returns a numeric for the variance of $\mu$

Author(s)

Stephan Wojciekowski

References

Neuenschwander, Beat, et al. "Robust exchangeability designs for early phase clinical trials with multiple strata." Pharmaceutical statistics 15.2 (2016): 123-134.

Examples

  getMuVar(response_rate = 0.3,
           tau_scale     = 1)
  getMuVar(response_rate = 0.3,
           tau_scale     = 1,
           n_worth       = 2)
getMuVar(response_rate = 0.3,
           tau_scale     = 1)
  getMuVar(response_rate = 0.3,
           tau_scale     = 1,
           n_worth       = 2)

getPriorParameters

Description

This function provides default prior parameters for the analysis methods that can be used in performAnalyses.

Usage

getPriorParameters(
  method_names,
  target_rates,
  n_worth = 1,
  tau_scale = 1,
  w_j = 0.5
)
getPriorParameters(
  method_names,
  target_rates,
  n_worth = 1,
  tau_scale = 1,
  w_j = 0.5
)

Arguments

`method_names`	A vector of strings for the names of the methods to be used. Available methods: `c("berry", "exnex", "exnex_adj", "pooled", "stratified")`
`target_rates`	A vector of numerics in `⁠(0, 1)⁠` for the target rate of each cohort
`n_worth`	An integer for the number of subjects the variability of the prior should reflect response rate scale, Default: `1`
`tau_scale`	A numeric for the scale parameter of the Half-normal distribution of $\tau$ in the methods `"berry"`, `"exnex"`, and `"exnex_adj"`, Default: `1`
`w_j`	A numeric in `⁠(0, 1)⁠` for the weight of the Ex component in the methods `"exnex"` and `"exnex_adj"`, Default: `0.5`

Details

Regarding the default prior parameters for "berry", "exnex", and "exnex_adj":

"berry": The mean of $\mu$ is set to 0. Its variance is calculated as proposed in "Robust exchangeability designs for early phase clinical trials with multiple strata" (Neuenschwander et al. (2016)) with regard to n_worth. The scale parameter of $\tau$ is set to tau_scale.
"exnex": The weight of the Ex component is set to w_j. For the Ex component: The target rate that results in the greatest variance is determined. The mean of $\mu$ is set to that target rate. The variance of $\mu$ is calculated as proposed in "Robust exchangeability designs for early phase clinical trials with multiple strata" (Neuenschwander et al. (2016)) with regard to n_worth. The scale parameter of $\tau$ is set to tau_scale. For the Nex components: The means of $\mu_j$ are set to the respective target rates. The variances of $\tau_j$ are calculated as proposed in "Robust exchangeability designs for early phase clinical trials with multiple strata" (Neuenschwander et al. (2016)) with regard to n_worth, see also getMuVar.
"exnex_adj": The weight of the Ex component is set to w_j. For the Ex component: The target rate that results in the greatest variance is determined. The mean of $\mu$ is set to 0. The variance of $\mu$ is calculated as proposed in "Robust exchangeability designs for early phase clinical trials with multiple strata" (Neuenschwander et al. (2016)) with regard to n_worth, see also getMuVar. The scale parameter of $\tau$ is set to tau_scale. For the Nex components: The means of $\mu_j$ are set to the 0. The variances of $\tau_j$ are calculated as proposed in "Robust exchangeability designs for early phase clinical trials with multiple strata" (Neuenschwander et al. (2016)) with regard to n_worth, see also getMuVar.
"pooled": The target rate that results in the greatest variance is determined. The scale parameter $\alpha$ is set to that target rate times n_worth. The scale parameter $\beta$ is set to 1 - that target rate times n_worth.
"stratified": The scale parameters $\alpha_j$ are set to target_rates * n_worth. The scale parameters $\beta_j$ are set to (1 - target_rates) * n_worth.

Value

A list with prior parameters of class prior_parameters_list

Author(s)

Stephan Wojciekowski

References

Berry, Scott M., et al. "Bayesian hierarchical modeling of patient subpopulations: efficient designs of phase II oncology clinical trials." Clinical Trials 10.5 (2013): 720-734.

Neuenschwander, Beat, et al. "Robust exchangeability designs for early phase clinical trials with multiple strata." Pharmaceutical statistics 15.2 (2016): 123-134.

Examples

prior_parameters_list <- getPriorParameters(
  method_names = c("berry", "exnex", "exnex_adj", "pooled", "stratified"),
  target_rates = c(0.1, 0.2, 0.3))
prior_parameters_list <- getPriorParameters(
  method_names = c("berry", "exnex", "exnex_adj", "pooled", "stratified"),
  target_rates = c(0.1, 0.2, 0.3))

invLogit

Description

This function returns the inverse logit of the input argument.

Usage

invLogit(theta)
invLogit(theta)

Arguments

theta

A numeric

Details

This function is an alias for 'stats::binomial()$linkinv'

Value

Inverse logit of theta

Author(s)

Stephan Wojciekowski

Examples

invLogit(logit(0.3))
invLogit(c(-Inf, 0, Inf))
invLogit(logit(0.3))
invLogit(c(-Inf, 0, Inf))

loadAnalyses

Description

This function loads an analysis performed with performAnalyses

Usage

loadAnalyses(
  scenario_numbers,
  analysis_numbers = rep(1, length(scenario_numbers)),
  load_path = tempdir()
)
loadAnalyses(
  scenario_numbers,
  analysis_numbers = rep(1, length(scenario_numbers)),
  load_path = tempdir()
)

Arguments

`scenario_numbers`	A (vector of) positive integer(s) for the scenario number(s)
`analysis_numbers`	A (vector of) positive integer(s) for the analysis number(s), Default: `rep(1, length(scenario_numbers))`
`load_path`	A string providing a path where the scenarios are being stored, Default: `tempfile`

Value

Returns an object of class analysis_list

Author(s)

Stephan Wojciekowski

Examples

  trial_data <- createTrial(
    n_subjects   = c(10, 20, 30),
    n_responders = c(1, 2, 3))

  analysis_list <- performAnalyses(
    scenario_list      = trial_data,
    target_rates       = rep(0.5, 3),
    n_mcmc_iterations  = 100)

  save_info     <- saveAnalyses(analysis_list)
  analysis_list <- loadAnalyses(scenario_numbers = save_info$scenario_numbers,
                                analysis_numbers = save_info$analysis_numbers,
                                load_path        = save_info$path)
trial_data <- createTrial(
    n_subjects   = c(10, 20, 30),
    n_responders = c(1, 2, 3))

  analysis_list <- performAnalyses(
    scenario_list      = trial_data,
    target_rates       = rep(0.5, 3),
    n_mcmc_iterations  = 100)

  save_info     <- saveAnalyses(analysis_list)
  analysis_list <- loadAnalyses(scenario_numbers = save_info$scenario_numbers,
                                analysis_numbers = save_info$analysis_numbers,
                                load_path        = save_info$path)

loadScenarios

Description

This function loads scenarios saved with saveScenarios

Usage

loadScenarios(scenario_numbers, load_path = tempdir())
loadScenarios(scenario_numbers, load_path = tempdir())

Arguments

`scenario_numbers`	A vector of integers naming the scenario to be loaded
`load_path`	A string for the directory where the scenarios are being stored, Default: `tempfile`

Value

Returns an object of class scenario_list

Author(s)

Stephan Wojciekowski

Examples

  scenarios_list <- simulateScenarios(
    n_subjects_list     = list(c(10, 20, 30)),
    response_rates_list = list(rep(0.9, 3)),
    n_trials            = 10)

  save_info      <- saveScenarios(scenarios_list)
  scenarios_list <- loadScenarios(scenario_numbers = save_info$scenario_numbers,
                                  load_path        = save_info$path)
scenarios_list <- simulateScenarios(
    n_subjects_list     = list(c(10, 20, 30)),
    response_rates_list = list(rep(0.9, 3)),
    n_trials            = 10)

  save_info      <- saveScenarios(scenarios_list)
  scenarios_list <- loadScenarios(scenario_numbers = save_info$scenario_numbers,
                                  load_path        = save_info$path)

logit

Description

This function returns the logit of the input argument.

Usage

logit(p)
logit(p)

Arguments

`p`	A numeric in (0, 1)

Details

This function is an alias for 'stats::binomial()$linkfun'

Value

logit of p

Author(s)

Stephan Wojciekowski

Examples

logit(invLogit(0.3))
logit(c(0, 0.5, 1))
logit(invLogit(0.3))
logit(c(0, 0.5, 1))

negateGoDecisions

Description

Negates the go decisions derived with getGoDecisions.

Usage

negateGoDecisions(go_decisions_list, overall_min_nogos = "all")
negateGoDecisions(go_decisions_list, overall_min_nogos = "all")

Arguments

`go_decisions_list`	An object of class `decision_list`, as returned by `getGoDecisions`
`overall_min_nogos`	Either a non-negative integer or the string `all` for the minimum number of cohort-level NoGo decisions required for an overall NoGo decision, Default: `all`

Details

This function is intended for implementing decision rules with a consider zone as e.g. proposed in "Bayesian design of proof-of-concept trials" by Fisch et al. (2015). This approach involves two criteria, Significance and Relevance.

Significance: high evidence that the treatment effect is greater than some smaller value (e.g. treatment effect under H0)
Relevance: moderate evidence that the treatment effect is greater than some larger value (e.g. treatment effect under a certain alternative)

The decision for a cohort is then taken as follows:

Go decision: Significance and Relevance
Consider decision: either Significance, or Relevance, but not both
NoGo decision: no Significance and no Relevance

In the example below, the following criteria for are implemented for each of the three cohorts:

Significance: $P(p_j > 0.4) > 0.95$
Relevance: $P(p_j > 0.8) > 0.5$

Value

A list of NoGo decisions of class decision_list

Author(s)

Stephan Wojciekowski

References

Fisch, Roland, et al. "Bayesian design of proof-of-concept trials." Therapeutic innovation & regulatory science 49.1 (2015): 155-162.

Examples

scenarios_list <- simulateScenarios(
  n_subjects_list     = list(c(10, 20, 30)),
  response_rates_list = list(rep(0.9, 3)),
  n_trials            = 10)

analysis_list <- performAnalyses(
  scenario_list      = scenarios_list,
  target_rates       = rep(0.5, 3),
  n_mcmc_iterations  = 100)

go_decisions_list <- getGoDecisions(
  analyses_list   = analysis_list,
  cohort_names    = c("p_1", "p_2", "p_3",
                      "p_1", "p_2", "p_3"),
  evidence_levels = c(0.5,  0.5,  0.5,
                      0.95, 0.95, 0.95),
  boundary_rules  = quote(c(x[1] > 0.8 & x[4] > 0.4,
                            x[2] > 0.8 & x[5] > 0.4,
                            x[3] > 0.8 & x[6] > 0.4)))

nogo_decisions <- negateGoDecisions(getGoDecisions(
  analyses_list   = analysis_list,
  cohort_names    = c("p_1", "p_2", "p_3",
                      "p_1", "p_2", "p_3"),
  evidence_levels = c(0.5,  0.5,  0.5,
                      0.95, 0.95, 0.95),
  boundary_rules  = quote(c(x[1] > 0.8 | x[4] > 0.4,
                            x[2] > 0.8 | x[5] > 0.4,
                            x[3] > 0.8 | x[6] > 0.4))))

getGoProbabilities(go_decisions_list, nogo_decisions)
scenarios_list <- simulateScenarios(
  n_subjects_list     = list(c(10, 20, 30)),
  response_rates_list = list(rep(0.9, 3)),
  n_trials            = 10)

analysis_list <- performAnalyses(
  scenario_list      = scenarios_list,
  target_rates       = rep(0.5, 3),
  n_mcmc_iterations  = 100)

go_decisions_list <- getGoDecisions(
  analyses_list   = analysis_list,
  cohort_names    = c("p_1", "p_2", "p_3",
                      "p_1", "p_2", "p_3"),
  evidence_levels = c(0.5,  0.5,  0.5,
                      0.95, 0.95, 0.95),
  boundary_rules  = quote(c(x[1] > 0.8 & x[4] > 0.4,
                            x[2] > 0.8 & x[5] > 0.4,
                            x[3] > 0.8 & x[6] > 0.4)))

nogo_decisions <- negateGoDecisions(getGoDecisions(
  analyses_list   = analysis_list,
  cohort_names    = c("p_1", "p_2", "p_3",
                      "p_1", "p_2", "p_3"),
  evidence_levels = c(0.5,  0.5,  0.5,
                      0.95, 0.95, 0.95),
  boundary_rules  = quote(c(x[1] > 0.8 | x[4] > 0.4,
                            x[2] > 0.8 | x[5] > 0.4,
                            x[3] > 0.8 | x[6] > 0.4))))

getGoProbabilities(go_decisions_list, nogo_decisions)

performAnalyses

Description

This function performs the analysis of simulated or observed trial data with the specified methods and returns the quantiles of the posterior response rates

Usage

performAnalyses(
  scenario_list,
  evidence_levels = c(0.025, 0.05, 0.5, 0.8, 0.9, 0.95, 0.975),
  method_names = c("berry", "exnex", "exnex_adj", "pooled", "stratified"),
  target_rates = NULL,
  prior_parameters_list = NULL,
  calc_differences = NULL,
  n_mcmc_iterations = 10000,
  n_cores = 1,
  seed = 1,
  verbose = TRUE
)
performAnalyses(
  scenario_list,
  evidence_levels = c(0.025, 0.05, 0.5, 0.8, 0.9, 0.95, 0.975),
  method_names = c("berry", "exnex", "exnex_adj", "pooled", "stratified"),
  target_rates = NULL,
  prior_parameters_list = NULL,
  calc_differences = NULL,
  n_mcmc_iterations = 10000,
  n_cores = 1,
  seed = 1,
  verbose = TRUE
)

Arguments

`scenario_list`	An object of class `scenario_list`, as e.g. created with `simulateScenarios`
`evidence_levels`	A vector of numerics in `⁠(0, 1)⁠` for the `1-evidence_levels`-quantiles of the posterior response rates to be saved. Default: `c(0.025, 0.05, 0.5, 0.8, 0.9, 0.95, 0.975)`
`method_names`	A vector of strings for the names of the methods to be used. Must be one of the default values, Default: `c("berry", "exnex", "exnex_adj", "pooled", "stratified")`
`target_rates`	A vector of numerics in `⁠(0, 1)⁠` for the target rates of each cohort, Default: `NULL`
`prior_parameters_list`	An object of class `prior_parameters_list`, as e.g. created with `getPriorParameters`
`calc_differences`	A matrix of positive integers with 2 columns. For each row the differences will be calculated. Also a vector of positive integers can be provided for a single difference. The integers are the numbers for the cohorts to be subtracted from one another. E.g. providing `c(2, 1)` calculates the difference between cohort `2` and cohort `1`. If `NULL`, no subtractions are performed, Default: `NULL`
`n_mcmc_iterations`	A positive integer for the number of MCMC iterations, see Details, Default: `10000`. If `n_mcmc_iterations` is present in `.GlobalEnv` and `missing(n_mcmc_iterations)`, the globally available value will be used.
`n_cores`	Argument is deprecated and does nothing as of version 0.9.3. A positive integer for the number of cores for the parallelization, Default: `1`
`seed`	Argument is deprecated and does nothing as of version 0.9.3. A numeric for the random seed, Default: `1`
`verbose`	A logical indicating whether messages should be printed, Default: `TRUE`

Details

This function applies the following analysis models to (simulated) scenarios of class scenario_list:

Bayesian hierarchical model (BHM) proposed by Berry et al. (2013): "berry"
BHM proposed by Neuenschwander et al. (2016): "exnex"
BHM that combines above approaches: "exnex_adj"
Pooled beta-binomial approach: "pooled"
Stratified beta-binomial approach: "stratified"

The posterior distributions of the BHMs are approximated with Markov chain Monte Carlo (MCMC) methods implemented in JAGS. Two independent chains are used with each n_mcmc_iterations number of MCMC iterations. The first floor(n_mcmc_iterations / 3) number of iterations are discarded as burn-in period. No thinning is applied.

Note that the value for n_mcmc_iterations required for a good approximation of the posterior distributions depends on the analysis model, the investigated scenarios, and the use case. The default value might be a good compromise between run-time and approximation for the estimation of decision probabilities, but it should definitively be increased for the analysis of a single trial's outcome.

The analysis models will only be applied to the unique trial realizations across all simulated scenarios. The models can be applied in parallel by registering a parallel backend for the 'foreach' framework, e.g. with doFuture::registerDoFuture() and future::plan(future::multisession). The parallelization is nested, so that the resources of a HPC environment can be used efficiently. For more on this topic, kindly see the respective vignette. The tasks that are to be performed in parallel are chunked according to the number of workers determined with foreach::getDoParWorkers().

The JAGS code for the BHM "exnex" was taken from Neuenschwander et al. (2016). The JAGS code for the BHM "exnex_adj" is based on the JAGS code for "exnex".

Value

An object of class analysis_list.

Author(s)

Stephan Wojciekowski

References

Berry, Scott M., et al. "Bayesian hierarchical modeling of patient subpopulations: efficient designs of phase II oncology clinical trials." Clinical Trials 10.5 (2013): 720-734.

Neuenschwander, Beat, et al. "Robust exchangeability designs for early phase clinical trials with multiple strata." Pharmaceutical statistics 15.2 (2016): 123-134.

Plummer, Martyn. "JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling." Proceedings of the 3rd international workshop on distributed statistical computing. Vol. 124. No. 125.10. 2003.

Examples

 trial_data <- createTrial(
   n_subjects   = c(10, 20, 30),
   n_responders = c(1, 2, 3))

 analysis_list <- performAnalyses(
   scenario_list      = trial_data,
   target_rates       = rep(0.5, 3),
   calc_differences   = matrix(c(3, 2, 1, 1), ncol = 2),
   n_mcmc_iterations  = 100)
trial_data <- createTrial(
   n_subjects   = c(10, 20, 30),
   n_responders = c(1, 2, 3))

 analysis_list <- performAnalyses(
   scenario_list      = trial_data,
   target_rates       = rep(0.5, 3),
   calc_differences   = matrix(c(3, 2, 1, 1), ncol = 2),
   n_mcmc_iterations  = 100)

saveAnalyses

Description

This function saves an object of class analysis_list

Usage

saveAnalyses(analyses_list, save_path = tempdir(), analysis_numbers = NULL)
saveAnalyses(analyses_list, save_path = tempdir(), analysis_numbers = NULL)

Arguments

`analyses_list`	An object of class `analysis_list`, as created with `performAnalyses`
`save_path`	A string for the path where the scenarios are being stored, Default: `tempfile`
`analysis_numbers`	A positive integer naming the analysis number. If `NULL`, the function will look for the number of saved analyses of the scenario in the directory and add 1, Default: `NULL`

Value

A named list of length 3 of vectors with scenario and analysis numbers and the save_path

Author(s)

Stephan Wojciekowski

Examples

  trial_data <- createTrial(
    n_subjects   = c(10, 20, 30),
    n_responders = c(1, 2, 3))

  analysis_list <- performAnalyses(
    scenario_list      = trial_data,
    target_rates       = rep(0.5, 3),
    n_mcmc_iterations  = 100)

  save_info     <- saveAnalyses(analysis_list)
  analysis_list <- loadAnalyses(scenario_numbers = save_info$scenario_numbers,
                                analysis_numbers = save_info$analysis_numbers,
                                load_path        = save_info$path)
trial_data <- createTrial(
    n_subjects   = c(10, 20, 30),
    n_responders = c(1, 2, 3))

  analysis_list <- performAnalyses(
    scenario_list      = trial_data,
    target_rates       = rep(0.5, 3),
    n_mcmc_iterations  = 100)

  save_info     <- saveAnalyses(analysis_list)
  analysis_list <- loadAnalyses(scenario_numbers = save_info$scenario_numbers,
                                analysis_numbers = save_info$analysis_numbers,
                                load_path        = save_info$path)

saveScenarios

Description

Saves the scenario data in a newly created or existing directory

Usage

saveScenarios(scenario_list, save_path = tempdir())
saveScenarios(scenario_list, save_path = tempdir())

Arguments

`scenario_list`	An object of class `scenario_list`, e.g. created with `simulateScenarios()`
`save_path`	A string providing the path for the directory in which the directory of the scenario should be created, Default: `tempfile`

Value

A named list of length 2 with the scenario numbers and the save_path

Author(s)

Stephan Wojciekowski

Examples

  scenarios_list <- simulateScenarios(
    n_subjects_list     = list(c(10, 20, 30)),
    response_rates_list = list(rep(0.9, 3)),
    n_trials            = 10)

  save_info      <- saveScenarios(scenarios_list)
  scenarios_list <- loadScenarios(scenario_numbers = save_info$scenario_numbers,
                                  load_path        = save_info$path)
scenarios_list <- simulateScenarios(
    n_subjects_list     = list(c(10, 20, 30)),
    response_rates_list = list(rep(0.9, 3)),
    n_trials            = 10)

  save_info      <- saveScenarios(scenarios_list)
  scenarios_list <- loadScenarios(scenario_numbers = save_info$scenario_numbers,
                                  load_path        = save_info$path)

scaleRoundList

Description

This function applies scaling and rounding to each item of a list of numerics

Usage

scaleRoundList(list, scale_param = 1, round_digits = NULL)
scaleRoundList(list, scale_param = 1, round_digits = NULL)

Arguments

`list`	The list to which the scaling and rounding should be applied to.
`scale_param`	A numeric for the scaling of each item of the list, Default: '1'
`round_digits`	An integer for the number of digits. If 'NULL', no rounding will be applied, Default: 'NULL'

Value

A list of scaled and rounded numerics

Author(s)

Stephan Wojciekowski

Examples

some_list <- as.list(runif(5))
scaleRoundList(some_list, scale_param = 100, round_digits = 2)

scenarios_list <- simulateScenarios(
  n_subjects_list     = list(c(10, 20, 30)),
  response_rates_list = list(c(0.1, 0.2, 0.3)),
  n_trials            = 10)

analyses_list <- performAnalyses(
  scenario_list       = scenarios_list,
  target_rates        = rep(0.5, 3),
  n_mcmc_iterations   = 100)

scaleRoundList(
  list         = getEstimates(analyses_list),
  scale_param  = 100,
  round_digits = 2)
some_list <- as.list(runif(5))
scaleRoundList(some_list, scale_param = 100, round_digits = 2)

scenarios_list <- simulateScenarios(
  n_subjects_list     = list(c(10, 20, 30)),
  response_rates_list = list(c(0.1, 0.2, 0.3)),
  n_trials            = 10)

analyses_list <- performAnalyses(
  scenario_list       = scenarios_list,
  target_rates        = rep(0.5, 3),
  n_mcmc_iterations   = 100)

scaleRoundList(
  list         = getEstimates(analyses_list),
  scale_param  = 100,
  round_digits = 2)

setPriorParametersBerry

Description

This function sets prior parameters for the analysis method "berry" for use in performAnalyses.

Usage

setPriorParametersBerry(mu_mean, mu_sd, tau_scale)
setPriorParametersBerry(mu_mean, mu_sd, tau_scale)

Arguments

`mu_mean`	A numeric for the mean of $\mu$
`mu_sd`	A positive numeric for the standard deviation of $\mu$
`tau_scale`	A positive numeric for the scale parameter of $\tau$

Details

This function sets the prior parameters for the method proposed by Berry et al. (2013). Note that the implemented distribution of $\tau$ is half-normal.

Value

A list with prior parameters of class prior_parameters_list

Author(s)

Stephan Wojciekowski

References

Berry, Scott M., et al. "Bayesian hierarchical modeling of patient subpopulations: efficient designs of phase II oncology clinical trials." Clinical Trials 10.5 (2013): 720-734.

Examples

 prior_parameters_berry <- setPriorParametersBerry(0, 1, 2)
prior_parameters_berry <- setPriorParametersBerry(0, 1, 2)

setPriorParametersExNex

Description

This function sets prior parameters for the analysis method "exnex" for use in performAnalyses.

Usage

setPriorParametersExNex(mu_mean, mu_sd, tau_scale, mu_j, tau_j, w_j)
setPriorParametersExNex(mu_mean, mu_sd, tau_scale, mu_j, tau_j, w_j)

Arguments

`mu_mean`	A numeric for the mean of $\mu$
`mu_sd`	A positive numeric for the standard deviation of $\mu$
`tau_scale`	A positive numeric for the scale parameter of $\tau$
`mu_j`	A vector of numerics for the means $\mu_j$
`tau_j`	A vector of positive numerics for the standard deviations $\tau_j$
`w_j`	A numeric in `⁠(0, 1)⁠` for the weight of the Ex component

Details

This function sets the prior parameters for the method proposed by Neuenschwander et al. (2016).

Value

A list with prior parameters of class prior_parameters_list

Author(s)

Stephan Wojciekowski

References

Neuenschwander, Beat, et al. "Robust exchangeability designs for early phase clinical trials with multiple strata." Pharmaceutical statistics 15.2 (2016): 123-134.

Examples

 prior_parameters_exnex <- setPriorParametersExNex(0, 1, 2, c(4, 5), c(6, 7), 0.8)
prior_parameters_exnex <- setPriorParametersExNex(0, 1, 2, c(4, 5), c(6, 7), 0.8)

setPriorParametersExNexAdj

Description

This function sets prior parameters for the analysis method "exnex_adj" for use in performAnalyses.

Usage

setPriorParametersExNexAdj(mu_mean, mu_sd, tau_scale, mu_j, tau_j, w_j)
setPriorParametersExNexAdj(mu_mean, mu_sd, tau_scale, mu_j, tau_j, w_j)

Arguments

`mu_mean`	A numeric for the mean of $\mu$
`mu_sd`	A positive numeric for the standard deviation of $\mu$
`tau_scale`	A positive numeric for the scale parameter of $\tau$
`mu_j`	A vector of numerics for the means $\mu_j$
`tau_j`	A vector of positive numerics for the standard deviations $\tau_j$
`w_j`	A numeric in `⁠(0, 1)⁠` for the weight of the Ex component

Details

This function sets the prior parameters for the method ExNex Adjusted, which combines the approach proposed by Neuenschwander et al. (2016) and the approach proposed by Berry et al. (2013).

Value

A list with prior parameters of class prior_parameters_list

Author(s)

Stephan Wojciekowski

Examples

 prior_parameters_exnex_adj <- setPriorParametersExNexAdj(0, 1, 2, c(4, 5), c(6, 7), 0.8)
prior_parameters_exnex_adj <- setPriorParametersExNexAdj(0, 1, 2, c(4, 5), c(6, 7), 0.8)

setPriorParametersPooled

Description

This function sets prior parameters for the analysis method "pooled" for use in performAnalyses.

Usage

setPriorParametersPooled(a, b)
setPriorParametersPooled(a, b)

Arguments

`a`	A positive numeric for $\alpha$
`b`	A positive numeric for $\beta$

Details

The method "pooled" is a beta-binomial model that pools all cohorts. The prior parameters are the scale parameters of the beta prior distribution.

Value

A list with prior parameters of class prior_parameters_list

Author(s)

Stephan Wojciekowski

Examples

 prior_parameters_pooled <- setPriorParametersPooled(1, 2)
prior_parameters_pooled <- setPriorParametersPooled(1, 2)

setPriorParametersStratified

Description

This function sets prior parameters for the analysis method "stratified" for use in performAnalyses.

Usage

setPriorParametersStratified(a_j, b_j)
setPriorParametersStratified(a_j, b_j)

Arguments

`a_j`	A vector of positive numerics for $\alpha$
`b_j`	A vector of positive numerics for $\beta$

Details

The method "stratified" is a beta-binomial model that assesses each cohort individually. The prior parameters are the scale parameters of the beta prior distributions.

Value

A list with prior parameters of class prior_parameters_list

Author(s)

Stephan Wojciekowski

Examples

 prior_parameters_pooled <- setPriorParametersStratified(c(1, 2), c(3, 4))
prior_parameters_pooled <- setPriorParametersStratified(c(1, 2), c(3, 4))

simulateScenarios

Description

This function creates scenarios for the analysis with performAnalyses.

Usage

simulateScenarios(
  n_subjects_list,
  response_rates_list,
  scenario_numbers = seq_along(response_rates_list),
  n_trials = 10000
)
simulateScenarios(
  n_subjects_list,
  response_rates_list,
  scenario_numbers = seq_along(response_rates_list),
  n_trials = 10000
)

Arguments

`n_subjects_list`	A list that contains for each scenario a vector for the number of subjects per cohort. A single vector can be provided if all scenarios should have the same number of subjects.
`response_rates_list`	A list that contains for each scenario a vector for the response rates per cohort.
`scenario_numbers`	A vector of positive integers naming the scenarios, Default: `seq_along(response_rates_list)`
`n_trials`	An integer indicating the number of trial simulations per response rates, Default: `10000`. If `n_trials` is present in `.GlobalEnv` and `missing(n_trials)`, the globally available value will be used.

Details

The function simulates trials with binary outcome for each scenario. Integer values for the response rates will be treated as observed outcomes.

Value

An object of class scenario_list with the scenario data for each specified scenario.

Author(s)

Stephan Wojciekowski

Examples

  n_subjects     <- c(10, 20, 30)

  rr_negative    <- rep(0.1, 3)
  rr_nugget      <- c(0.9, 0.1, 0.1)
  rr_positive    <- rep(0.9, 3)

  scenarios_list <- simulateScenarios(
    n_subjects_list     = list(n_subjects,
                               n_subjects,
                               n_subjects),
    response_rates_list = list(rr_negative,
                               rr_nugget,
                               rr_positive))
n_subjects     <- c(10, 20, 30)

  rr_negative    <- rep(0.1, 3)
  rr_nugget      <- c(0.9, 0.1, 0.1)
  rr_positive    <- rep(0.9, 3)

  scenarios_list <- simulateScenarios(
    n_subjects_list     = list(n_subjects,
                               n_subjects,
                               n_subjects),
    response_rates_list = list(rr_negative,
                               rr_nugget,
                               rr_positive))

Package 'bhmbasket'

Help Index

combinePriorParameters

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

continueRecruitment

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

createTrial

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

getEstimates

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

getGoDecisions

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

getGoProbabilities

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

getMuVar

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

getPriorParameters

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

invLogit

Description

Usage

Arguments

Details