Bland-Altman Method Comparison.

blandr: a Bland-Altman Method Comparison package for R

blandr carries out Bland Altman analyses (also known as a Tukey mean-difference plot) as described by JM Bland and DG Altman in 1986. This package was started in 2015 as existing Bland-Altman R functions did not calculate confidence intervals. blandr has been improved iteratively over time, and hopefully will be useful as an open-source library for reproducible science.

The benefits

• Calculates and plots confidence intervals
• Outputs ggplot2 information to create extensible plots
• Associated Jamovi module, allowing researchers and users uncomfortable with command line tools to use a graphical user interface
• Shiny/rmarkdown-based automatic report generating function
• Developing functions to chart for proportional bias
• Open source and on GitHub - anyone can contribute and extend the library

Citations

You can find the citation information through the usual R citation commands:

citation("blandr")

The DOI will refer to all versions of blandr. If you need to cite specific releases DOIs, the full versioning information can be found at Zenodo (https://zenodo.org/record/824514), with the full source code at the blandr GitHub page (https://github.com/deepankardatta/blandr/).

Installation

blandr is available as a package from CRAN and can be installed with the following commands:

install.packages("blandr")
library(blandr)

You can install the blandr development version, hosted on github at https://github.com/deepankardatta/blandr/, with the following commands:

install.packages("devtools")
devtools::install_github("deepankardatta/blandr")

Example

This is a basic example which shows you how to solve a common problem:

library(blandr)
load(file="Data/bland.altman.PEFR.1986.rda")
blandr.display ( bland.altman.PEFR.1986$WrightFirst , bland.altman.PEFR.1986$MiniWrightFirst , sig.level=0.95 )
blandr.draw( bland.altman.PEFR.1986$WrightFirst , bland.altman.PEFR.1986$MiniWrightFirst )

The Jamovi module

One of the benefits of blandr is that the library has been developed so a module can be generated for the Jamovi GUI statistical spreadsheet (https://www.jamovi.org/). I’m a believer in making this tool more accesible and Jamovi is a way to do this. The Jamovi module will also be iteratively developed - the code for each Jamovi version is linked to the CRAN version on GitHub. The module will be on the Jamovi library.

If you want to use the development versions of blandr, you can install Jamovi from the comman line using the following commands:

install.packages('jmvtools', repos=c('https://repo.jamovi.org', 'https://cran.r-project.org'))
library(jmvtools)
jmvtools::install()

You can then download the blandr GitHub development branch and install it to Jamovi from the command line.

Why the odd name?

Thinking of a unique name was difficult - if you look through the history of the sources the package had a very cumbersome name. This had to change when uploading to CRAN as it seemed to be frowned upon.

Hadley Wickham has an excellent set of documentation on creating packages. The one on naming (http://r-pkgs.had.co.nz/package.html) is worth a read. Reflecting on it a lot of the naming issues are to prevent collisions with similarly named packages, so using blandaltman in the name might have cause problems. Mirroring the pre-existing plyr and knitr I thought I’d just add a “r” to “bland”: yes it doesn’t involve Altman’s name, but I couldn’t think of anything better.

If you can think of a better name please let me know!

Further information

Further information can be found in the function help files in the package, as well as in the vignettes.

System Requirements

The library is quite small, and mostly depends on the ggplot2 library. However the report functions do require rmarkdown, knitr, and most importantly pandoc - the function should check this is available and stop gracefully if not. Further the Jamovi based functions need the Jamovi library to work.

Future improvements

Whilst this package is good enough for use, there is the scope for iterative improvements.

Future works include:

• There are a further few deprecated functions to delete (I just need to finish a few projects first!!).
• The package needs to have to go through some validation and testing
• For further testing I need to write some testhtat modules
• The function descriptions needs to be improved
• Some of the roxygen2 documentation can be improved by calling the import parameters function
• I want to add a few more sample data packs: including some of my own if possible
• Development of a report generator
• Further development of the Jamovi module

Help wanted!

Comments on the code, suggestions for improvement, verification tests, validation studies, and even code contributions would be gratefully accepted. Email me at deepankar(dot)datta(at)gmail.com and I’ll try and get back to you as soon as possible. Please do bear in mind this is a side-project, and I can be otherwise busy with a lot of other work.