Repository overview

This repository provides a reproducible analytical pipeline (RAP) that implements a real-life stroke service capacity planning model in R. It serves as a practical example, fully reproducible and aligned with RAP principles, based on the following study:

Monks T, Worthington D, Allen M, Pitt M, Stein K, James MA. A modelling tool for capacity planning in acute and community stroke services. BMC Health Serv Res. 2016 Sep 29;16(1):530. doi: 10.1186/s12913-016-1789-4. PMID: 27688152; PMCID: PMC5043535.

Model diagram:

Installation

Clone the repository:

{.r} git clone https://github.com/pythonhealthdatascience/rdesrap_stroke.git cd rdesrap_stroke

Set up the R environment using renv (recommended):

{.r} renv::init() renv::restore()

If you encounter issues restoring the exact environment, you can install dependencies from DESCRIPTION and generate your own lock file:

{.r} renv::init() renv::install() renv::snapshot()

How to run

The simulation code is in the R/ folder as a local package. There are files executing the model and analysing the results in rmarkdown/.

Install the local package:

```{.r} devtools::install() # Needed if running in parallel devtools::load_all() # Sufficient if running sequentially

library(simulation) ```

Run a single simulation:

{.r} param <- create_parameters(number_of_runs = 1L) single_results <- runner(param = param)

Run multiple replications:

{.r} param <- create_parameters(number_of_runs = 5L) single_results <- runner(param = param)

Run all analyses (from command line):

{.r} bash run_rmarkdown.sh

Run tests:

{.r} devtools::test()

Compute test coverage:

{.r} devtools::test_coverage()

Lint code:

{.r} lintr::lint_dir()

Generating the results from the article

The original study used Simul8. Each of the outputs from that article have been replicated in this repository using R:

• Figure 1. Simulation probability density function for occupancy of an acute stroke unit.
• Figure 3. Simulated trade-off between the probability that a patient is delayed and the no. of acute beds available.
• Table 2. Likelihood of delay. Current admissions versus 5% more admissions.
• Table 3. Results of pooling of acute and rehab beds.
• Supplementary Table 1. Likelihood of delay. Current admissions versus No Complex neurological patients.
• Supplementary Table 3. Likelihood of delay. Current admissions versus ring fenced acute stroke beds.

To generate these, simply execute rmarkdown/analysis.Rmd.

Examples

Figure 1

Original:

From this repository:

Figure 3

Original:

From this repository:

Run time and machine specification

The run time for this analysis (notebooks/analysis.Rmd) is 1m 38s seconds. This was on an Intel Core i7-12700H, 32GB RAM, Ubuntu 24.04.1.

The other notebooks generate results for tests and illustrate other functionality (e.g. importing parameters from csv, running with logs), and these just take a second or two.

Citation

If you use this repository, please cite either the GitHub repository or Zenodo:

Heather, A. (2025). Stroke capacity planning model: R DES RAP. GitHub. https://github.com/pythonhealthdatascience/rdesrap_stroke.

Heather, A. (2025). Stroke capacity planning model: R DES RAP. Zenodo. https://doi.org/10.5281/zenodo.15863376.

Contributors:

Amy Heather - developed the repository.

Licence

MIT Licence. See LICENSE.md for details.

Funding

This project was developed as part of the project STARS: Sharing Tools and Artefacts for Reproducible Simulations. It is supported by the Medical Research Council [grant number MR/Z503915/1].