Package index • DTEAssurance

All functions

INTEREST: INTEREST data set
MCMC_sample: MCMC_sample
PP_func(): Calculate the predictive probability (PP) given interim data and posterior samples
REVEL: REVEL data set
ZODIAC: ZODIAC data set
add_recruitment_time(): Add recruitment time to a survival dataset
apply_GSD_to_trial(): Apply a group-sequential design to one simulated trial
apply_design_rule(): Apply a design rule to paired replicate output
assurance_adaptive_shiny_app(): Launch the 'shiny' adaptive assurance app
assurance_shiny_app(): Launch the 'shiny' Assurance app
calc_dte_assurance(): Calculate Assurance for a Trial with a Delayed Treatment Effect
calc_dte_assurance_adaptive(): Calculates operating characteristics for a Group Sequential Trial with a Delayed Treatment Effect
calibrate_PP_threshold(): Function to calculate the 'optimal' PP threshold value
calibrate_PP_timing(): Function to calculate the 'optimal' information fraction to calculate PP
calibrate_matched_futility_boundary(): Calibrate a fixed Z-statistic futility boundary
cens_data(): Censor a survival dataset
compute_relative_floors(): Relative power floors for kappa selection
make_gsd_design() make_rpact_design_from_GSD_model(): Build the group-sequential design for a GSD_model
run_calibration_grid(): Simulate PP values and true trial outcomes for PP-threshold calibration
run_paired_scenario(): Run paired replicates for one scenario, with checkpointing
select_kappa_star(): Select the PP futility threshold minimising null expected sample size
sim_dte(): Simulates survival times for a delayed treatment effect (DTE) scenario, where the treatment group experiences a delayed onset of benefit. Control and treatment groups are generated under exponential or Weibull distributions.
single_paired_rep(): Simulate one paired replicate for post hoc design evaluation
summarize_grid_by_kappa(): Summarise calibration output over a grid of PP futility thresholds
survival_test(): Calculate statistical significance on a survival dataset
update_priors(): Update prior distributions using interim survival data