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The goldilocks package implements the Goldilocks
adaptive trial design described in Broglio et al. (2014). This vignette
provides a visual overview of how the package functions are
interconnected.
The diagram below shows the call graph from the top-level simulation
function (sim_trials()) down through the core engine
(survival_adapt()) and into the internal analysis
pipeline.
Exported functions are shown in blue. Internal functions are shown in grey.
The functions fall into three layers:
sim_trials(): Top-level entry point.
Runs survival_adapt() across multiple trials (optionally in
parallel) and collates results.summarise_sims(): Summarizes the
output of sim_trials(), computing operating characteristics
such as power, expected sample size, and stopping probabilities.survival_adapt(): Simulates a single
adaptive trial. Generates data via sim_comp_data(),
conducts interim analyses using posterior() and
test_stop_success(), and performs the final analysis via
test_final().sim_comp_data(): Generates a complete
trial dataset by calling enrollment(),
randomization(), and pwe_sim().posterior(): Estimates the posterior
distribution of piecewise exponential hazard rates using a conjugate
Gamma model.analyse_data(): Applies the chosen
analysis method (logrank, cox,
bayes, or chisq) to an (imputed) dataset.impute_data(): Imputes missing event
times for censored subjects using pwe_impute() or
pwe_sim().haz_to_prop(): Converts posterior
hazard rate draws to cumulative incidence proportions via
ppwe().These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.