check_performance {adaptr}

Check performance metrics for trial simulations

Description

Calculates performance metrics for a trial specification based on simulation results from the run_trials() function, with bootstrapped uncertainty measures if requested. Uses extract_results(), which may be used directly to extract key trial results without summarising. This function is also used by summary() to calculate the performance metrics presented by that function.

Usage

check_performance(
  object,
  select_strategy = "control if available",
  select_last_arm = FALSE,
  select_preferences = NULL,
  te_comp = NULL,
  raw_ests = FALSE,
  final_ests = NULL,
  restrict = NULL,
  uncertainty = FALSE,
  n_boot = 5000,
  ci_width = 0.95,
  boot_seed = 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.

restrict

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.

uncertainty

single logical; if FALSE (default) uncertainty measures are not calculated, if TRUE, non-parametric bootstrapping is used to calculate uncertainty measures.

n_boot

single integer (default 5000); the number of bootstrap samples to use if uncertainty = TRUE. Values ⁠< 100⁠ are not allowed and values ⁠< 1000⁠ will lead to a warning, as results are likely to be unstable in those cases.

ci_width

single numeric ⁠>= 0⁠ and ⁠< 1⁠, the width of the percentile-based bootstrapped confidence intervals. Defaults to 0.95, corresponding to 95% confidence intervals.

boot_seed

single integer, NULL (default), or "base". If a value is provided, this value will be used to initiate random seeds when bootstrapping with the global random seed restored after the function has run. If "base" is specified, the base_seed specified in run_trials() is used. Regardless of whether simulations are run sequentially or in parallel, bootstrapped results will be identical if a boot_seed is specified.

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.

Details

The ideal design percentage (IDP) returned is based on Viele et al, 2020 doi:10.1177/1740774519877836 (and also described in Granholm et al, 2022 doi:10.1016/j.jclinepi.2022.11.002, which also describes the other performance measures) and has been adapted to work for trials with both desirable/undesirable outcomes and non-binary outcomes. Briefly, the expected outcome is calculated as the sum of the true outcomes in each arm multiplied by the corresponding selection probabilities (ignoring simulations with no selected arm). The IDP is then calculated as:

• For desirable outcomes (highest_is_best is TRUE):
  ⁠100 * (expected outcome - lowest true outcome) / (highest true outcome - lowest true outcome)⁠

• For undesirable outcomes (highest_is_best is FALSE):
  ⁠100 - IDP calculated for desirable outcomes⁠

Value

A tidy data.frame with added class trial_performance (to control the number of digits printed, see print()), with the columns "metric" (described below), "est" (estimate of each metric), and the following four columns if uncertainty = TRUE: "err_sd"(bootstrapped SDs), "err_mad" (bootstrapped MAD-SDs, as described in setup_trial() and stats::mad()), "lo_ci", and "hi_ci", the latter two corresponding to the lower/upper limits of the percentile-based bootstrapped confidence intervals. Bootstrap estimates are not calculated for the minimum (⁠_p0⁠) and maximum values (⁠_p100⁠) of size, sum_ys, and ratio_ys, as non-parametric bootstrapping for minimum/maximum values is not sensible - bootstrap estimates for these values will be NA.
The following performance metrics are calculated:

• n_summarised: the number of simulations summarised.

• size_mean, size_sd, size_median, size_p25, size_p75, size_p0, size_p100: the mean, standard deviation, median as well as 25-, 75-, 0- (min), and 100- (max) percentiles of the sample sizes (number of patients randomised in each simulated trial) of the summarised trial simulations.

• sum_ys_mean, sum_ys_sd, sum_ys_median, sum_ys_p25, sum_ys_p75, sum_ys_p0, sum_ys_p100: the mean, standard deviation, median as well as 25-, 75-, 0- (min), and 100- (max) percentiles of the total sum_ys across all arms in the summarised trial simulations (e.g., the total number of events in trials with a binary outcome, or the sums of continuous values for all patients across all arms in trials with a continuous outcome). Always uses all outcomes 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_mean, ratio_ys_sd, ratio_ys_median, ratio_ys_p25, ratio_ys_p75, ratio_ys_p0, ratio_ys_p100: the mean, standard deviation, median as well as 25-, 75-, 0- (min), and 100- (max) percentiles of the final ratio_ys (sum_ys as described above divided by the total number of patients randomised) across all arms in the summarised trial simulations.

• prob_conclusive: the proportion (0 to 1) of conclusive trial simulations, i.e., simulations not stopped at the maximum sample size without a superiority, equivalence or futility decision.

• prob_superior, prob_equivalence, prob_futility, prob_max: the proportion (0 to 1) of trial simulations stopped for superiority, equivalence, futility or inconclusive at the maximum allowed sample size, respectively.
  Note: Some metrics may not make sense if summarised simulation results are restricted.

• ⁠prob_select_*⁠: the selection probabilities for each arm and for no selection, according to the specified selection strategy. Contains one element per arm, named ⁠prob_select_arm_<arm name>⁠ and prob_select_none for the probability of selecting no arm.

• rmse, rmse_te: the root mean squared errors of the estimates for the selected arm and for the treatment effect, as described in extract_results().

• mae, mae_te: the median absolute errors of the estimates for the selected arm and for the treatment effect, as described in extract_results().

• idp: the ideal design percentage (IDP; 0-100%), see Details.

See Also

extract_results(), 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)

# Check performance measures, without assuming that any arm is selected in
# the inconclusive simulations, with bootstrapped uncertainty measures
# (unstable in this example due to the very low number of simulations
# summarised):
check_performance(res, select_strategy = "none", uncertainty = TRUE,
n_boot = 1000, boot_seed = "base")