Simulate a single trial — run

This function conducts a single trial simulation using a trial specification as specified by setup_trial(), setup_trial_binom() or setup_trial_norm().
During simulation, the function randomises "patients", randomly generates outcomes, calculates the probabilities that each arm is the best (and better than the control, if any). This is followed by checking inferiority, superiority, equivalence and/or futility as desired; dropping arms, and re-adjusting allocation probabilities according to the criteria specified in the trial specification. If there is no common control arm, the trial simulation will be stopped at the final specified adaptive analysis, when 1 arm is superior to the others, or when all arms are considered equivalent (if equivalence is assessed). If a common control arm is specified, all other arms will be compared to that, and if 1 of these pairwise comparisons crosses the applicable superiority threshold at an adaptive analysis, that arm will become the new control and the old control will be considered inferior and dropped. If multiple non-control arms cross the applicable superiority threshold in the same adaptive analysis, the one with the highest probability of being the overall best will become the new control. Equivalence/futility will also be checked if specified, and equivalent or futile arms will be dropped in designs with a common control arm and the entire trial will be stopped if all remaining arms are equivalent in designs without a common control arm. The trial simulation will be stopped when only 1 arm is left, when the final arms are all equivalent, or after the final specified adaptive analysis.
After stopping (regardless of reason), a final analysis including outcome data from all patients randomised to all arms will be conducted (with the final control arm, if any, used as the control in this analysis). Results from this analysis will be saved, but not used with regards to the adaptive stopping rules. This is particularly relevant if less patients have available outcome data at the last adaptive analyses than the total number of patients randomised (as specified in setup_trial(), setup_trial_binom(), or setup_trial_norm()), as the final analysis will then include all patients randomised, which may be more than in the last adaptive analysis conducted.

Usage

run_trial(trial_spec, seed = NULL, sparse = FALSE)

Arguments

trial_spec: trial_spec object, generated and validated by the setup_trial(), setup_trial_binom() or setup_trial_norm() function.
seed: single integer or NULL (default). If a value is provided, this value will be used as the random seed when running and the global random seed will be restored after the function has run, so it is not affected.
sparse: single logical; if FALSE (default) everything listed below is included in the returned object. If TRUE, only a limited amount of data are included in the returned object. This can be practical when running many simulations and saving the results using the run_trials() function (which relies on this function), as the output file will thus be substantially smaller. However, printing of individual trial results will be substantially less detailed for sparse results and non-sparse results are required by plot_history().

Value

A trial_result object containing everything listed below if sparse (as described above) is FALSE. Otherwise only final_status, final_n, followed_n, trial_res, seed, and sparse are included.

final_status: either "superiority", "equivalence", "futility", or "max" (stopped at the last possible adaptive analysis), as calculated during the adaptive analyses.
final_n: the total number of patients randomised.
followed_n: the total number of patients with available outcome data at the last adaptive analysis conducted.
max_n: the pre-specified maximum number of patients with outcome data available at the last possible adaptive analysis.
max_randomised: the pre-specified maximum number of patients randomised at the last possible adaptive analysis.
looks: numeric vector, the total number of patients with outcome data available at each conducted adaptive analysis.
planned_looks: numeric vector, the cumulated number of patients planned to have outcome data available at each adaptive analysis, even those not conducted if the simulation is stopped before the final possible analysis.
randomised_at_looks: numeric vector, the cumulated number of patients randomised at each conducted adaptive analysis (only including the relevant numbers for the analyses actually conducted).
start_control: character, initial common control arm (if specified).
final_control: character, final common control arm (if relevant).
control_prob_fixed: fixed common control arm probabilities (if specified; see setup_trial()).
inferiority, superiority, equivalence_prob, equivalence_diff, equivalence_only_first, futility_prob, futility_diff, futility_only_first, highest_is_best, and soften_power: as specified in setup_trial().
best_arm: the best arm(s), as described in setup_trial().
trial_res: a data.frame containing most of the information specified for each arm in setup_trial() including true_ys (true outcomes as specified in setup_trial()) and for each arm the sum of the outcomes (sum_ys/sum_ys_all; i.e., the total number of events for binary outcomes or the totals of continuous outcomes) and sum of patients (ns/ns_all), summary statistics for the raw outcome data (raw_ests/raw_ests_all, calculated as specified in setup_trial(), defaults to mean values, i.e., event rates for binary outcomes or means for continuous outcomes) and posterior estimates (post_ests/post_ests_all, post_errs/post_errs_all, lo_cri/lo_cri_all, and hi_cri/hi_cri_all, calculated as specified in setup_trial()), final_status of each arm ("inferior", "superior", "equivalence", "futile", "active", or "control" (currently active control arm, including if the current control when stopped for equivalence)), status_look (specifying the cumulated number of patients with outcome data available when an adaptive analysis changed the final_status to "superior", "inferior", "equivalence", or "futile"), status_probs, the probability (in the last adaptive analysis for each arm) that each arm was the best/better than the common control arm (if any)/equivalent to the common control arm (if any and stopped for equivalence; NA if the control arm was stopped due to the last remaining other arm(s) being stopped for equivalence)/futile if stopped for futility at the last analysis it was included in, final_alloc, the final allocation probability for each arm the last time patients were randomised to it, including for arms stopped at the maximum sample size, and probs_best_last, the probabilities of each remaining arm being the overall best in the last conducted adaptive analysis (NA for previously dropped arms).
Note: for the variables in the data.frame where a version including the _all-suffix is included, the versions WITHOUT this suffix are calculated using patients with available outcome data at the time of analysis, while the versions WITH the _all-suffixes are calculated using outcome data for all patients randomised at the time of analysis, even if they have not reached the time of follow-up yet (see setup_trial()).
all_looks: a list of lists containing one list per conducted trial look (adaptive analysis). These lists contain the variables arms, old_status (status before the analysis of the current round was conducted), new_status (as specified above, status after current analysis has been conducted), sum_ys/sum_ys_all (as described above), ns/ns_all (as described above), old_alloc (the allocation probability used during this look), probs_best (the probabilities of each arm being the best in the current adaptive analysis), new_alloc (the allocation probabilities after updating these in the current adaptive analysis; NA for all arms when the trial is stopped and no further adaptive analyses will be conducted), probs_better_first (if a common control is provided, specifying the probabilities that each arm was better than the control in the first analysis conducted during that look), probs_better (as probs_better_first, but updated if another arm becomes the new control), probs_equivalence_first and probs_equivalence (as for probs_better/probs_better_first, but for equivalence if equivalence is assessed). The last variables are NA if the arm was not active in the applicable adaptive analysis or if they would not be included during the next adaptive analysis.
allocs: a character vector containing the allocations of all patients in the order of randomization.
ys: a numeric vector containing the outcomes of all patients in the order of randomization (0 or 1 for binary outcomes).
seed: the random seed used, if specified.
description, add_info, cri_width, n_draws, robust: as specified in setup_trial(), setup_trial_binom() or setup_trial_norm().
sparse: single logical, corresponding to the sparse input.

Examples

# Setup a trial specification
binom_trial <- setup_trial_binom(arms = c("A", "B", "C", "D"),
                                 true_ys = c(0.20, 0.18, 0.22, 0.24),
                                 data_looks = 1:20 * 100)

# Run trial with a specified random seed
res <- run_trial(binom_trial, seed = 12345)

# Print results with 3 decimals
print(res, digits = 3)
#> Single simulation result: generic binomially distributed outcome trial
#> * Undesirable outcome
#> * No common control arm
#> 
#> Final status: inconclusive, stopped at final allowed adaptive analysis
#> Final/maximum allowed sample sizes: 2000/2000 (100.0%)
#> Available outcome data at last adaptive analysis: 2000/2000 (100.0%)
#> 
#> Trial results overview:
#>  arms true_ys final_status status_look status_probs final_alloc
#>     A    0.20       active          NA           NA      0.0232
#>     B    0.18       active          NA           NA      0.8868
#>     C    0.22       active          NA           NA      0.0900
#>     D    0.24     inferior         300       0.0092      0.1078
#> 
#> Esimates from final analysis (all patients):
#>  arms sum_ys_all ns_all raw_ests_all post_ests_all post_errs_all lo_cri_all
#>     A         39    161        0.242         0.244       0.03355      0.184
#>     B        297   1613        0.184         0.184       0.00987      0.165
#>     C         39    180        0.217         0.218       0.03032      0.162
#>     D         16     46        0.348         0.351       0.06806      0.224
#>  hi_cri_all
#>       0.316
#>       0.204
#>       0.279
#>       0.495
#> 
#> Estimates from last adaptive analysis including each arm:
#>  arms sum_ys   ns raw_ests post_ests post_errs lo_cri hi_cri
#>     A     39  161    0.242     0.244   0.03461  0.180  0.315
#>     B    297 1613    0.184     0.184   0.00938  0.166  0.204
#>     C     39  180    0.217     0.219   0.03105  0.164  0.283
#>     D     16   46    0.348     0.353   0.06967  0.226  0.492
#> 
#> Simulation details:
#> * Random seed: 12345
#> * Credible interval width: 95%
#> * Number of posterior draws: 5000
#> * Posterior estimation method: medians with MAD-SDs