Package index • goldilocks

Package

Core functions for simulating and summarizing Goldilocks adaptive trial designs.

survival_adapt(): Simulate and execute a single adaptive clinical trial design with a time-to-event endpoint
sim_trials(): Simulate one or more clinical trials subject to known design parameters and treatment effect
summarise_sims(): Summarize simulations to get operating characteristics
plot_sim_ocs(): Plot operating characteristics across simulation scenarios
plot_sim_decisions(): Plot predictive-probability decision maps
summarise_trial_trace(): Summarize an interim decision path
plot_trial_trace(): Plot predictive probabilities and enrollment at interim looks
plot_sim_stopping(): Plot stopping outcomes from trial simulations
print(<goldilocks_trial>): Print an adaptive trial trace result

Simulate complete trial datasets, including enrollment, randomization, and time-to-event outcomes.

sim_comp_data(): Simulate a complete clinical trial with event data drawn from a piecewise exponential distribution
enrollment(): Simulate exact continuous-time enrollment
randomization(): Randomization allocation

Functions for simulating, imputing, and computing distributions under the piecewise exponential model.

pwe_sim(): Simulate piecewise exponential time-to-event outcomes
pwe_impute(): Impute piecewise exponential time-to-event outcomes
ppwe(): Cumulative distribution function of the PWE for a vectorized hazard rate parameter
prop_to_haz(): Estimate plausible piecewise constant hazard rates from summary summary event proportions