Calculate the upper bound probability of false positive — upper_bound_proba_FP

This function calculates the upper bound probability of false positive based on the model, prior probability of no benefit, source data, theta_0, target sample size per arm, case study configuration, number of replicates, confidence level, null space, and critical value.

Usage

upper_bound_proba_FP_MC(
  model,
  prior_proba_no_benefit,
  source_data,
  theta_0,
  target_sample_size_per_arm,
  case_study_config,
  target_to_source_std_ratio,
  dropout_probability = 0,
  event_time_distribution = "exponential",
  treatment_delay = 0,
  n_replicates,
  confidence_level,
  null_space,
  critical_value,
  case_study,
  method,
  n_samples_quantiles_estimation
)

Arguments

model: The model.
prior_proba_no_benefit: The prior probability of no benefit.
source_data: The source data.
theta_0: The value of theta_0.
target_sample_size_per_arm: The target sample size per arm.
case_study_config: The case study configuration.
target_to_source_std_ratio: Ratio between target and source sampling standard deviations.
dropout_probability: Probability of loss to follow-up over the maximum follow-up time. Only used for the time-to-event endpoint.
event_time_distribution: Distribution of the event times, either "exponential" or "weibull". Only used for the time-to-event endpoint.
treatment_delay: Time before the treatment effect starts, in years. Only used for the time-to-event endpoint.
n_replicates: The number of replicates.
confidence_level: The confidence level.
null_space: The null space (either "left" or "right").
critical_value: The critical value.
case_study: Case study name.
method: Method name.
n_samples_quantiles_estimation: Number of samples used to estimate distribution quantiles.

Value

The upper bound probability of false positive, defined as \(Pr(Study success|\theta_T = \theta_0) \times Pr(\theta_T \leq \theta_0)\)

Examples

NA
#> [1] NA