Estimates success, stopping, and sample-size operating characteristics from a collection of simulated trials and quantifies their Monte Carlo uncertainty.
summarise_sims(data, max_mcse = NULL)A data frame reporting the operating characteristics, including the
power (which will be equal to the type I error in the null case); the
proportion of trials that declared immediate success, stopped accrual for
expected success, stopped for futility, or went to the maximum sample size.
stop_success retains its historical meaning of stopping accrual for
expected success; stop_any_success combines both success-stopping
decisions. The average stopping sample size (and standard deviation) are
also recorded. The proportion of trials that stopped accrual for expected
success, yet went on to fail, is also reported. Each probability and mean
is accompanied by its Monte Carlo standard error and 95% Monte Carlo
confidence limits, with columns ending in _mcse, _mc_lower, and
_mc_upper. Probability intervals use the Wilson method; the mean
sample-size interval uses a t distribution.
These intervals describe uncertainty from using a finite number of simulated trials under fixed design and data-generating assumptions. They are not clinical confidence intervals, treatment-effect intervals, or measures of model uncertainty.
The output always reports n_used, the number of successfully analyzed
simulations used by the operating-characteristic estimands. When complete
sim_trials() results are supplied, it also reports requested, analyzed,
and failed counts, the failure rate, computational method, and seed. For a
raw simulation data frame, requested and failed counts are unknown and are
reported as NA. Details of the call, random-number generation, parallel
computation, timing, failures, and design assumptions are retained in the
simulation_metadata attribute.
A required complete result returned by sim_trials(), a
simulation data.frame, or a list of either form. Named list elements
identify scenarios. Existing scenario columns and grouping variables are
preserved.
NULL (the default), or a named numeric vector of finite,
positive values giving the largest acceptable Monte Carlo standard error
for selected estimands. Supported names are power,
stop_immediate_success, stop_success, stop_any_success,
stop_futility, stop_max_N, mean_N, stop_and_fail, and
failure_rate. A warning identifies every scenario and estimand whose
Monte Carlo standard error exceeds its target.