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README.Rmd
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---
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
## augbin_rheum
## [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/dwyl/esta/issues)
## Description
R package for analysing composite responder endpoints in rheumatology with up to two continuous and one binary components.
## Getting Started
Install from Github using `devtools::install_github("martinamcm/augbin_rheum")`
Implementation as a Shiny app with further documentation on functionality and examples available at [AugBin](https://github.com/martinamcm/AugBin)
## Details
Function `augbinrheum` is used to conduct the analysis with the following arguments:
* `Data` Data with columns ordered as follows: patient ID, treatment arm, continuous outcome(s),
binary outcome followed by the baseline continuous measure(s)
* `cts` Number of continuous outcomes in the composite which can be 1 or 2. Defaults to 1.
* `bin` Number of binary outcomes in the composite which can be 1 or 0. Defaults to 1.
* `dichot` Responder threshold in each of the continuous outcomes. For one continuous outcome
input as 'd1' and for two continuous outcomes input as 'c(d1,d2)'
## References
McMenamin M, Grayling MJ, Berglind A, Wason JMS. Increasing power in the analysis of responder endpoints in rheumatology: a software tutorial. *medRxiv*. 2020. doi: [10.1101/2020.07.28.20163378](https://www.medrxiv.org/content/10.1101/2020.07.28.20163378v1)
McMenamin M, Barrett JK, Berglind A, Wason JM. Employing a latent variable framework to improve efficiency in composite endpoint analysis. *Statistical Methods in Medical Research*. 2021;30(3):702-716. doi: [10.1177/0962280220970986](https://doi.org/10.1177/0962280220970986)