Package index • goldilocks

Package

Core functions for simulating and summarizing Goldilocks adaptive trial designs.

survival_adapt(): Simulate and analyze one Goldilocks adaptive trial
evaluate_interim(): Evaluate an externally observed interim data cut
sim_trials(): Estimate operating characteristics by trial simulation
summarise_sims(): Estimate operating characteristics from trial simulations
summarise_calendar_time(): Summarize operating characteristics on the calendar-time scale
plot_enrollment(): Plot an enrollment projection
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_interim>): Print an externally evaluated interim analysis
print(<goldilocks_trial>): Print a Goldilocks adaptive trial result
print(<goldilocks_calendar_summary>): Print a calendar-time operating-characteristic summary

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

sim_comp_data(): Simulate complete trial data under piecewise-exponential event rates
enrollment(): Simulate exact continuous-time enrollment
randomization(): Generate a block-randomized treatment sequence

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(): Calculate endpoint event probabilities from piecewise hazards
prop_to_haz(): Derive piecewise-constant hazard rates from cumulative event probabilities