R: Extract simulation results

extract_results {adaptr}

R Documentation

Extract simulation results

Description

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.

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")