plot_metrics_ecdf: Plot empirical cumulative distribution functions of...

In adaptr: Adaptive Trial Simulator

Plot empirical cumulative distribution functions of performance metrics

Description

Plots empirical cumulative distribution functions (ECDFs) of numerical performance metrics across multiple simulations from a "trial_results" object returned by run_trials(). Requires the ggplot2 package installed.

Usage

plot_metrics_ecdf(
  object,
  metrics = c("size", "sum_ys", "ratio_ys"),
  select_strategy = "control if available",
  select_last_arm = FALSE,
  select_preferences = NULL,
  te_comp = NULL,
  raw_ests = FALSE,
  final_ests = NULL,
  restrict = NULL,
  nrow = NULL,
  ncol = NULL,
  cores = NULL
)

Arguments

object	trial_results object, output from the run_trials() function.
metrics	the performance metrics to plot, as described in extract_results(). Multiple metrics may be plotted at the same time. Valid metrics include: size, sum_ys, ratio_ys_mean, sq_err, sq_err_te, err, err_te, abs_err, abs_err_te, (as described in extract_results(), with the addition of abs_err and abs_err_te, which are the absolute errors, i.e., abs(err) and abs(err_te)). All may be specified using either spaces or underlines (case sensitive). Defaults to plotting size, sum_ys, and ratio_ys_mean.
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.
nrow, ncol	the number of rows and columns when plotting multiple metrics in the same plot (using faceting in ggplot2). Defaults to NULL, in which case this will be determined automatically.
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

Note that the arguments related to arm selection and error calculation are only relevant if errors are visualised.

Value

A ggplot2 plot object.

See Also

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

Examples

#### Only run examples if ggplot2 is installed ####
if (requireNamespace("ggplot2", quietly = TRUE)){

  # 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 multiple simulation with a fixed random base seed
  res_mult <- run_trials(binom_trial, n_rep = 25, base_seed = 678)

  # NOTE: the number of simulations in this example is smaller than
  # recommended - the plots reflect that, and would likely be smoother if
  # a larger number of trials had been simulated

  # Plot ECDFs of continuous performance metrics
  plot_metrics_ecdf(res_mult)

}

adaptr documentation built on May 29, 2024, 7:48 a.m.