Prints contents of the first input x in a human-friendly way, see
Details for more information.
# S3 method for trial_spec
print(x, prob_digits = 3, ...)# S3 method for trial_result
print(x, prob_digits = 3, ...)
# S3 method for trial_performance
print(x, digits = 3, ...)
# S3 method for trial_results
print(
x,
select_strategy = "control if available",
select_last_arm = FALSE,
select_preferences = NULL,
te_comp = NULL,
raw_ests = FALSE,
final_ests = NULL,
restrict = NULL,
digits = 1,
cores = NULL,
...
)
# S3 method for trial_results_summary
print(x, digits = 1, ...)
# S3 method for trial_calibration
print(x, ...)
Invisibly returns x.
object to print, see Details.
single integer (default is 3), the number of digits used
when printing probabilities, allocation probabilities and softening powers
(with 2 extra digits added for stopping rule probability thresholds in
trial specifications and for outcome rates in summarised results from
multiple simulations).
additional arguments, not used.
single integer, the number of digits used when printing
the numeric results. Default is 3 for outputs from check_performance()
and 1 for outputs from run_trials() and the accompanying summary()
method.
single character string. If a trial was not stopped
due to superiority (or had only 1 arm remaining, if select_last_arm is
set to TRUE in trial designs with a common control arm; see below),
this parameter specifies which arm will be considered selected when
calculating trial design performance metrics, as described below;
this corresponds to the consequence of an inconclusive trial, i.e., which
arm would then be used in practice.
The following options are available and must be written exactly as below
(case sensitive, cannot be abbreviated):
"control if available" (default): selects the first
control arm for trials with a common control arm if this
arm is active at end-of-trial, otherwise no arm will be selected. For
trial designs without a common control, no arm will be selected.
"none": selects no arm in trials not ending with superiority.
"control": similar to "control if available", but will throw
an error if used for trial designs without a common control arm.
"final control": selects the final control arm regardless
of whether the trial was stopped for practical equivalence, futility,
or at the maximum sample size; this strategy can only be specified
for trial designs with a common control arm.
"control or best": selects the first control arm if still
active at end-of-trial, otherwise selects the best remaining arm
(defined as the remaining arm with the highest probability of being
the best in the last adaptive analysis conducted). Only works for
trial designs with a common control arm.
"best": selects the best remaining arm (as described under
"control or best").
"list or best": selects the first remaining arm from a specified
list (specified using select_preferences, technically a character
vector). If none of these arms are are active at end-of-trial, the best
remaining arm will be selected (as described above).
"list": as specified above, but if no arms on the provided list
remain active at end-of-trial, no arm is selected.
single logical, defaults to FALSE. If TRUE, the
only remaining active arm (the last control) will be selected in trials
with a common control arm ending with equivalence or futility, before
considering the options specified in select_strategy. Must be FALSE for
trial designs without a common control arm.
character vector specifying a number of arms used
for selection if one of the "list or best" or "list" options are
specified for select_strategy. Can only contain valid arms
available in the trial.
character string, treatment-effect comparator. Can be either
NULL (the default) in which case the first control arm is used for
trial designs with a common control arm, or a string naming a single trial
arm. Will be used when calculating err_te and sq_err_te (the error
and the squared error of the treatment effect comparing the selected arm to
the comparator arm, as described below).
single logical. If FALSE (default), the
posterior estimates (post_ests or post_ests_all, see setup_trial()
and run_trial()) will be used to calculate err and sq_err (the error
and the squared error of the estimated compared to the specified effect in
the selected arm) and err_te and sq_err_te (the error and the squared
error of the treatment effect comparing the selected arm to the comparator
arm, as described for te_comp and below). If TRUE, the raw estimates
(raw_ests or raw_ests_all, see setup_trial() and run_trial()) will
be used instead of the posterior estimates.
single logical. If TRUE (recommended) the final estimates
calculated using outcome data from all patients randomised when trials are
stopped are used (post_ests_all or raw_ests_all, see setup_trial()
and run_trial()); if FALSE, the estimates calculated for each arm when
an arm is stopped (or at the last adaptive analysis if not before) using
data from patients having reach followed up at this time point and not all
patients randomised are used (post_ests or raw_ests, see
setup_trial() and run_trial()). If NULL (the default), this argument
will be set to FALSE if outcome data are available immediate after
randomisation for all patients (for backwards compatibility, as final
posterior estimates may vary slightly in this situation, even if using the
same data); otherwise it will be said to TRUE. See setup_trial() for
more details on how these estimates are calculated.
single character string or NULL. If NULL (default),
results are summarised for all simulations; if "superior", results are
summarised for simulations ending with superiority only; if "selected",
results are summarised for simulations ending with a selected arm only
(according to the specified arm selection strategy for simulations not
ending with superiority). Some summary measures (e.g., prob_conclusive)
have substantially different interpretations if restricted, but are
calculated nonetheless.
NULL or single integer. If NULL, a default value set by
setup_cluster() will be used to control whether extractions of simulation
results are done in parallel on a default cluster or sequentially in the
main process; if a value has not been specified by setup_cluster(),
cores will then be set to the value stored in the global "mc.cores"
option (if previously set by options(mc.cores = <number of cores>), and
1 if that option has not been specified.
If cores = 1, computations
will be run sequentially in the primary process, and if cores > 1, a new
parallel cluster will be setup using the parallel library and removed
once the function completes. See setup_cluster() for details.
print(trial_spec): Trial specification
print(trial_result): Single trial result
print(trial_performance): Trial performance metrics
print(trial_results): Multiple trial results
print(trial_results_summary): Summary of multiple trial results
print(trial_calibration): Trial calibration
The behaviour depends on the class of x:
trial_spec: prints a trial specification setup by
setup_trial(), setup_trial_binom() or setup_trial_norm().
trial_result: prints the results of a single trial simulated by
run_trial(). More details are saved in the trial_result object and thus
printed if the sparse argument in run_trial() or run_trials() is set to
FALSE; if TRUE, fewer details are printed, but the omitted details are
available by printing the trial_spec object created by setup_trial(),
setup_trial_binom() or setup_trial_norm().
trial_results: prints the results of multiple simulations
generated using run_trials(). Further documentation on how multiple trials
are summarised before printing can be found in the summary() function
documentation.
trial_results_summary: print method for summary of multiple simulations
of the same trial specification, generated by using the summary() function
on an object generated by run_trials().