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Extract simulation results

Source: R/extract_results.R
extract_results.Rd

This function extracts relevant information from multiple simulations of the same trial specification in a tidy data.frame (1 simulation per row). See also the check_performance() and summary() functions, that uses the output from this function to further summarise simulation results.

Usage

extract_results(
  object,
  select_strategy = "control if available",
  select_last_arm = FALSE,
  select_preferences = NULL,
  te_comp = NULL,
  raw_ests = FALSE,
  final_ests = NULL,
  cores = NULL
)

Arguments

object

trial_results object, output from the run_trials() function.

select_strategy

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.

select_last_arm

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.

select_preferences

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.

te_comp

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).

raw_ests

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.

final_ests

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.

cores

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.

Value

A data.frame containing the following columns:

  • sim: the simulation number (from 1 to the total number of simulations).

  • final_n: the final sample size in each simulation.

  • sum_ys: the sum of the total counts in all arms, e.g., the total number of events in trials with a binary outcome (setup_trial_binom()) or the sum of the arm totals in trials with a continuous outcome (setup_trial_norm()). Always uses all outcome data from all randomised patients regardless of whether or not all patients had outcome data available at the time of trial stopping (corresponding to sum_ys_all in results from run_trial()).

  • ratio_ys: calculated as sum_ys/final_n (as described above).

  • final_status: the final trial status for each simulation, either "superiority", "equivalence", "futility", or "max", as described in run_trial().

  • superior_arm: the final superior arm in simulations stopped for superiority. Will be NA in simulations not stopped for superiority.

  • selected_arm: the final selected arm (as described above). Will correspond to the superior_arm in simulations stopped for superiority and be NA if no arm is selected. See select_strategy above.

  • err: the squared error of the estimate in the selected arm, calculated as estimated effect - true effect for the selected arm.

  • sq_err: the squared error of the estimate in the selected arm, calculated as err^2 for the selected arm, with err defined above.

  • err_te: the error of the treatment effect comparing the selected arm to the comparator arm (as specified in te_comp). Calculated as:
    (estimated effect in the selected arm - estimated effect in the comparator arm) - (true effect in the selected arm - true effect in the comparator arm)
    Will be NA for simulations without a selected arm, with no comparator specified (see te_comp above), and when the selected arm is the comparator arm.

  • sq_err_te: the squared error of the treatment effect comparing the selected arm to the comparator arm (as specified in te_comp), calculated as err_te^2, with err_te defined above.

See also

check_performance(), summary(), plot_convergence(), plot_metrics_ecdf(), check_remaining_arms().

Examples

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

# Run 10 simulations with a specified random base seed
res <- run_trials(binom_trial, n_rep = 10, base_seed = 12345)

# Extract results and Select the control arm if available
# in simulations not ending with superiority
extract_results(res, select_strategy = "control")
#>    sim final_n sum_ys ratio_ys final_status superior_arm selected_arm
#> 1    1    2000    387   0.1935          max         <NA>            A
#> 2    2    2000    391   0.1955          max         <NA>            A
#> 3    3    2000    359   0.1795          max         <NA>         <NA>
#> 4    4    2000    389   0.1945          max         <NA>            A
#> 5    5    2000    373   0.1865          max         <NA>            A
#> 6    6     400     84   0.2100  superiority            B            B
#> 7    7    2000    395   0.1975          max         <NA>            A
#> 8    8    2000    442   0.2210          max         <NA>            A
#> 9    9    2000    413   0.2065          max         <NA>            A
#> 10  10    2000    466   0.2330          max         <NA>            A
#>             err       sq_err     err_te sq_err_te
#> 1   0.027072360 7.329127e-04         NA        NA
#> 2   0.027225919 7.412507e-04         NA        NA
#> 3            NA           NA         NA        NA
#> 4   0.028619492 8.190753e-04         NA        NA
#> 5  -0.014477338 2.095933e-04         NA        NA
#> 6  -0.022699865 5.152839e-04 -0.1820624 0.0331467
#> 7   0.009098866 8.278937e-05         NA        NA
#> 8   0.010663973 1.137203e-04         NA        NA
#> 9   0.015544164 2.416210e-04         NA        NA
#> 10  0.019152691 3.668256e-04         NA        NA