plot_convergence {adaptr}R Documentation

Plot convergence of performance metrics


Plots performance metrics according to the number of simulations conducted for multiple simulated trials. The simulated trial results may be split into a number of batches to illustrate stability of performance metrics across different simulations. Calculations are done according to specified selection and restriction strategies as described in extract_results() and check_performance(). Requires the ggplot2 package installed.


  metrics = "size mean",
  resolution = 100,
  select_strategy = "control if available",
  select_last_arm = FALSE,
  select_preferences = NULL,
  te_comp = NULL,
  raw_ests = FALSE,
  final_ests = NULL,
  restrict = NULL,
  n_split = 1,
  nrow = NULL,
  ncol = NULL,
  cores = NULL



trial_results object, output from the run_trials() function.


the performance metrics to plot, as described in check_performance(). Multiple metrics may be plotted at the same time. Valid metrics include: size_mean, size_sd, size_median, size_p25, size_p75, size_p0, size_p100, sum_ys_mean, sum_ys_sd, sum_ys_median, sum_ys_p25, sum_ys_p75, sum_ys_p0, sum_ys_p100, ratio_ys_mean, ratio_ys_sd, ratio_ys_median, ratio_ys_p25, ratio_ys_p75, ratio_ys_p0, ratio_ys_p100, prob_conclusive, prob_superior, prob_equivalence, prob_futility, prob_max, ⁠prob_select_*⁠ (with * being either "⁠arm_<name>⁠ for all arm names or none), rmse, rmse_te, and idp. All may be specified as above, case sensitive, but with either spaces or underlines. Defaults to "size mean".


single positive integer, the number of points calculated and plotted, defaults to 100 and must be ⁠>= 10⁠. Higher numbers lead to smoother plots, but increases computation time. If the value specified is higher than the number of simulations (or simulations per split), the maximum possible value will be used instead.


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 sq_err_te (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 sq_err (the squared error of the estimated compared to the specified effect in the selected arm) and sq_err_te (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.


single positive integer, the number of consecutive batches the simulation results will be split into, which will be plotted separately. Default is 1 (no splitting); maximum value is the number of simulations summarised (after restrictions) divided by 10.

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.


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.


A ggplot2 plot object.

See Also

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


#### 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 show that performance metrics
  # are not stable and have likely not converged yet

  # Convergence plot of mean sample sizes
  plot_convergence(res_mult, metrics = "size mean")


if (requireNamespace("ggplot2", quietly = TRUE)){

  # Convergence plot of mean sample sizes and ideal design percentages,
  # with simulations split in 2 batches
  plot_convergence(res_mult, metrics = c("size mean", "idp"), n_split = 2)


[Package adaptr version 1.3.2 Index]