65 lines (50 with data), 2.2 kB

---
title: "biomkrAccrual"
author: "SJ Cowtan"
date: 2024-10-01
date-format: YYYY
output: 
  - rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{biomkrAccrual}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(biomkrAccrual)
```

Simulating recruitment at time of randomisation to adaptive trials with 
arm eligibility determined by biomarker status.

The `{biomkrAccrual}` package uses a Poisson-Gamma-Dirichlet model to simulate
trial recruitment for multi-site, multi-region, multi-arm trials. Recruitment per 
site is modelled with the Poisson-Gamma model (Anisimov and Federov, 2007).
A hierarchical Dirichlet model is used to model biomarker proportions for sites
within regions. Recruitment to a given site in a given week is then randomised
to biomarker status using the prevalences drawn from the Dirichlet model for that
site.

## Running a single simulation

`biomkrAccrual()`

The default settings will use the configuration files in the `extdata` directory, and will
keep the resulting data files and recruitment plots. The location can be specified with 
`output_path` and `figs_path`.

## Running a set of simulations

`biomkrAccrualSim(n = 250)`

The datafiles and recruitment plots from the individual runs will not be kept (this
can be changed with `quietly = FALSE`) but will preserve the summary datafiles and
distribution plots.

## Practical notes

There are a very large number of arguments to both commands, and three configuation files,
one of which (the relationship of treatment arms to biomarker recruitment arms) is a JSON.  
This is because flexibility is required, and they are intended to be driven by a dashboard 
in future.

This package will not pass R CMD Check because it is written using the new object orientation 
system for R, `{S7}`, and CMD Check does not yet understand the syntax.


Anisimov, V.V., Fedorov, V.V., 2007. Modelling, prediction and adaptive adjustment of 
recruitment in multicentre trials. Statistics in Medicine 26, 4958–4975. 
https://doi.org/10.1002/sim.2956