adaptr-package {adaptr} | R Documentation |

## adaptr: Adaptive Trial Simulator

### Description

*Adaptive Trial Simulator*

The `adaptr`

package simulates adaptive (multi-arm, multi-stage) randomised
clinical trials using adaptive stopping, adaptive arm dropping and/or
response-adaptive randomisation. The package is developed as part of the
INCEPT (Intensive Care Platform Trial) project,
funded primarily by a grant from
Sygeforsikringen "danmark".

### Details

The `adaptr`

package contains the following primary functions (in order of
typical use):

The

`setup_cluster()`

initiates a parallel computation cluster that can be used to run simulations and post-processing in parallel, increasing speed. Details on parallelisation and other options for running`adaptr`

functions in parallel are described in the`setup_cluster()`

documentation.The

`setup_trial()`

function is the general function that sets up a trial specification. The simpler, special-case functions`setup_trial_binom()`

and`setup_trial_norm()`

may be used for easier specification of trial designs using binary, binomially distributed or continuous, normally distributed outcomes, respectively, with some limitations in flexibility.The

`calibrate_trial()`

function calibrates a trial specification to obtain a certain value for a performance metric (typically used to calibrate the Bayesian type 1 error rate in a scenario with no between-arm differences), using the functions below.The

`run_trial()`

and`run_trials()`

functions are used to conduct single or multiple simulations, respectively, according to a trial specification setup as described in #2.The

`extract_results()`

,`check_performance()`

and`summary()`

functions are used to extract results from multiple trial simulations, calculate performance metrics, and summarise results. The`plot_convergence()`

function assesses stability of performance metrics according to the number of simulations conducted. The`plot_metrics_ecdf()`

function plots empirical cumulative distribution functions for numerical performance metrics. The`check_remaining_arms()`

function summarises all combinations of remaining arms across multiple trials simulations.The

`plot_status()`

and`plot_history()`

functions are used to plot the overall trial/arm statuses for multiple simulated trials or the history of trial metrics over time for single/multiple simulated trials, respectively.

For further information see the documentation of each function or the
**Overview** vignette (`vignette("Overview", package = "adaptr")`

) for an
example of how the functions work in combination.
For further examples and guidance on setting up trial specifications, see the
`setup_trial()`

documentation, the **Basic examples** vignette
(`vignette("Basic-examples", package = "adaptr")`

) and the
**Advanced example** vignette
(`vignette("Advanced-example", package = "adaptr")`

).

If using the package, please consider citing it using
`citation(package = "adaptr")`

.

### Author(s)

**Maintainer**: Anders Granholm andersgran@gmail.com (ORCID)

Authors:

Benjamin Skov Kaas-Hansen epiben@hey.com (ORCID)

Other contributors:

Aksel Karl Georg Jensen akje@sund.ku.dk (ORCID) [contributor]

Theis Lange thlan@sund.ku.dk (ORCID) [contributor]

### References

Granholm A, Jensen AKG, Lange T, Kaas-Hansen BS (2022). adaptr: an R package for simulating and comparing adaptive clinical trials. Journal of Open Source Software, 7(72), 4284. doi:10.21105/joss.04284

Granholm A, Kaas-Hansen BS, Lange T, Schjørring OL, Andersen LW, Perner A, Jensen AKG, Møller MH (2022). An overview of methodological considerations regarding adaptive stopping, arm dropping and randomisation in clinical trials. J Clin Epidemiol. doi:10.1016/j.jclinepi.2022.11.002

**Examples of studies using adaptr:**

Granholm A, Lange T, Harhay MO, Jensen AKG, Perner A, Møller MH, Kaas-Hansen BS (2023). Effects of duration of follow-up and lag in data collection on the performance of adaptive clinical trials. Pharm Stat. doi:10.1002/pst.2342

Granholm A, Lange T, Harhay MO, Perner A, Møller MH, Kaas-Hansen BS (2024). Effects of sceptical priors on the performance of adaptive clinical trials with binary outcomes. Pharm Stat. doi:10.1002/pst.2387

### See Also

`setup_cluster()`

, `setup_trial()`

, `setup_trial_binom()`

,
`setup_trial_norm()`

, `calibrate_trial()`

, `run_trial()`

, `run_trials()`

,
`extract_results()`

, `check_performance()`

, `summary()`

,
`check_remaining_arms()`

, `plot_convergence()`

, `plot_metrics_ecdf()`

,
`print()`

, `plot_status()`

, `plot_history()`

.

*adaptr*version 1.4.0 Index]