Advanced adaptive trial design methods for clinical research including Bayesian interim analysis, adaptive sample size re-estimation, futility stopping rules, and group sequential designs. Designed for efficient clinical trial conduct with ethical early stopping capabilities.
Usage
adaptivetrialdesign(
data,
outcome,
treatment,
outcomeLevel,
stratification_variables = NULL,
time_variable = NULL,
adaptation_type = "sample_size",
design_framework = "bayesian",
interim_analysis = TRUE,
interim_timing = "information",
interim_fractions = "0.25,0.50,0.75",
max_interim_analyses = 3,
efficacy_boundary = 0.95,
futility_boundary = 0.1,
boundary_type = "obrien_fleming",
planned_sample_size = 100,
minimum_effect_size = 0.5,
target_power = 0.8,
type1_error_rate = 0.05,
max_sample_size_inflation = 2,
prior_type = "weakly_informative",
historical_data_weight = 0.1,
prior_parameters = "",
decision_criterion = "posterior_probability",
bayes_factor_threshold = 3,
predictive_power_threshold = 0.8,
run_simulations = TRUE,
n_simulations = 1000,
true_effect_scenarios = "0,0.3,0.5,0.8",
mcmc_samples = 5000,
mcmc_burnin = 2000,
mcmc_chains = 3,
show_design_summary = TRUE,
show_interim_results = TRUE,
show_stopping_boundaries = TRUE,
show_sample_size_evolution = TRUE,
show_operating_characteristics = TRUE,
show_posterior_evolution = TRUE,
show_decision_analysis = TRUE,
show_interpretation = TRUE,
alpha_spending_function = "obf_type",
spending_parameter = 1,
blinded_sample_size_reestimation = FALSE,
conditional_power_threshold = 0.2,
clinical_context = "general",
set_seed = TRUE,
seed_value = 42,
regulatory_framework = "general",
dmb_recommendations = TRUE
)Arguments
- data
The data as a data frame for adaptive trial design analysis.
- outcome
.
- treatment
.
- outcomeLevel
.
- stratification_variables
.
- time_variable
For time-to-event endpoints
- adaptation_type
.
- design_framework
.
- interim_analysis
.
- interim_timing
.
- interim_fractions
Comma-separated fractions of planned information/sample size
- max_interim_analyses
.
- efficacy_boundary
Posterior probability threshold for efficacy stopping
- futility_boundary
Posterior probability threshold for futility stopping
- boundary_type
.
- planned_sample_size
.
- minimum_effect_size
Standardized or raw effect size
- target_power
.
- type1_error_rate
.
- max_sample_size_inflation
Maximum fold increase from planned sample size
- prior_type
.
- historical_data_weight
Weight given to historical data in informative priors
- prior_parameters
Comma-separated prior parameters (mean,sd for normal)
- decision_criterion
.
- bayes_factor_threshold
.
- predictive_power_threshold
.
- run_simulations
.
- n_simulations
.
- true_effect_scenarios
Comma-separated effect sizes for simulation
- mcmc_samples
.
- mcmc_burnin
.
- mcmc_chains
.
- show_design_summary
.
- show_interim_results
.
- show_stopping_boundaries
.
- show_sample_size_evolution
.
- show_operating_characteristics
.
- show_posterior_evolution
.
- show_decision_analysis
.
- show_interpretation
.
- alpha_spending_function
.
- spending_parameter
.
- blinded_sample_size_reestimation
.
- conditional_power_threshold
.
- clinical_context
.
- set_seed
.
- seed_value
.
- regulatory_framework
.
- dmb_recommendations
Generate templates for Data Monitoring Board recommendations
Value
A results object containing:
results$designSummary | a table | ||||
results$interimResults | a table | ||||
results$stoppingBoundaries | a table | ||||
results$sampleSizeEvolution | a table | ||||
results$operatingCharacteristics | a table | ||||
results$posteriorEvolution | a table | ||||
results$decisionAnalysis | a table | ||||
results$bayesFactors | a table | ||||
results$predictivePower | a table | ||||
results$regulatoryConsiderations | a table | ||||
results$dmbRecommendations | a table | ||||
results$clinicalInterpretation | a html | ||||
results$methodsExplanation | a html | ||||
results$stoppingBoundaryPlot | an image | ||||
results$sampleSizeEvolutionPlot | an image | ||||
results$posteriorEvolutionPlot | an image | ||||
results$operatingCharacteristicsPlot | an image | ||||
results$decisionAnalysisPlot | an image | ||||
results$predictivePowerPlot | an image |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$designSummary$asDF
as.data.frame(results$designSummary)