adaptr-package {adaptr}

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):

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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 [e-mail] (ORCID)

Authors:

• Benjamin Skov Kaas-Hansen [e-mail] (ORCID)

Other contributors:

• Aksel Karl Georg Jensen [e-mail] (ORCID) [contributor]

• Theis Lange [e-mail] (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

Website/manual

GitHub repository

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().